{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Importing Libraries","metadata":{}},{"cell_type":"code","source":"! pip install biosppy    ","metadata":{"execution":{"iopub.status.busy":"2021-07-23T14:02:46.198456Z","iopub.execute_input":"2021-07-23T14:02:46.198788Z","iopub.status.idle":"2021-07-23T14:02:56.578501Z","shell.execute_reply.started":"2021-07-23T14:02:46.198703Z","shell.execute_reply":"2021-07-23T14:02:56.577403Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Collecting biosppy\n  Downloading biosppy-0.7.3.tar.gz (85 kB)\n\u001b[K     |████████████████████████████████| 85 kB 331 kB/s eta 0:00:01\n\u001b[?25hCollecting bidict\n  Downloading bidict-0.21.2-py2.py3-none-any.whl (37 kB)\nRequirement already satisfied: h5py in /opt/conda/lib/python3.7/site-packages (from biosppy) (2.10.0)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from biosppy) (3.4.1)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.19.5)\nRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from biosppy) (0.24.1)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.5.4)\nRequirement already satisfied: shortuuid in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.0.1)\nRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.15.0)\nRequirement already satisfied: joblib in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.0.1)\nRequirement already satisfied: opencv-python in /opt/conda/lib/python3.7/site-packages (from biosppy) (4.5.1.48)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (1.3.1)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (2.8.1)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (7.2.0)\nRequirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (2.4.7)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (0.10.0)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->biosppy) (2.1.0)\nBuilding wheels for collected packages: biosppy\n  Building wheel for biosppy (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for biosppy: filename=biosppy-0.7.3-py2.py3-none-any.whl size=95409 sha256=2b718244b728ca40217ed327fccbb35af1f8c27d74e0c8792160402fa16c9e79\n  Stored in directory: /root/.cache/pip/wheels/2f/4f/8f/28b2adc462d7e37245507324f4817ce1c64ef2464f099f4f0b\nSuccessfully built biosppy\nInstalling collected packages: bidict, biosppy\nSuccessfully installed bidict-0.21.2 biosppy-0.7.3\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport wfdb\nimport os                                                                                                         \nimport gc\nimport scipy       \nimport sklearn\nfrom pathlib import Path\nfrom sklearn.utils import shuffle\nfrom sklearn.manifold import TSNE\nimport seaborn as sns\nfrom sklearn import preprocessing\nimport shutil\nimport math\nimport random\nfrom scipy.spatial import distance\nfrom biosppy.signals import ecg\nfrom scipy.interpolate import PchipInterpolator","metadata":{"execution":{"iopub.status.busy":"2021-07-23T14:02:56.580001Z","iopub.execute_input":"2021-07-23T14:02:56.580282Z","iopub.status.idle":"2021-07-23T14:03:02.870238Z","shell.execute_reply.started":"2021-07-23T14:02:56.580248Z","shell.execute_reply":"2021-07-23T14:03:02.869289Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"try:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  # TPU detection\n    print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])\nexcept ValueError:\n    raise BaseException('ERROR: Not connected to a TPU runtime; please see the previous cell in this notebook for instructions!')\n\ntf.config.experimental_connect_to_cluster(tpu)\ntf.tpu.experimental.initialize_tpu_system(tpu)\ntpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T05:35:24.714845Z","iopub.execute_input":"2021-06-24T05:35:24.718052Z","iopub.status.idle":"2021-06-24T05:35:31.160822Z","shell.execute_reply.started":"2021-06-24T05:35:24.717934Z","shell.execute_reply":"2021-06-24T05:35:31.159845Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Dataset Creation","metadata":{}},{"cell_type":"code","source":"####### Dataset Creation \n\n###### Constants\nFS = 500\nW_LEN = 256\nW_LEN_1_4 = 256 // 4\nW_LEN_3_4 = 3 * (256 // 4)\n\n###### Function to Read a Record\ndef read_rec(rec_path):\n\n    \"\"\" \n    Function to read record and return Segmented Signals\n\n    INPUTS:-\n    1) rec_path : Path of the Record\n\n    OUTPUTS:-\n    1) seg_sigs : Final Segmented Signals\n\n    \"\"\"\n    number_of_peaks = 2 # For extracting the required number of peaks                                    \n    full_rec = (wfdb.rdrecord(rec_path)).p_signal[:,1] # Entire Record\n\n    f = PchipInterpolator(np.arange(10000),full_rec) # Fitting Interpolation Function\n    x_samp = (np.arange(10000)*(500/360))[:7200] # Fixing Interpolation Input Values\n    full_rec_interp = f(x_samp)  # Intepolating Values \n    r_peaks_init = ecg.hamilton_segmenter(full_rec_interp,360)[0] # R-Peak Segmentation and input is the signal frequency of 500Hz in this case\n    final_peak_index = r_peaks_init[int(r_peaks_init.shape[0] - int((r_peaks_init.shape[0]%number_of_peaks)))-1]\n    r_peaks_final = r_peaks_init[:final_peak_index] # Final Number of R_Peaks\n    full_rec_final = full_rec_interp[:int(r_peaks_final[-1]+W_LEN)] # Final Sequence\n    seg_sigs, r_peaks_ref = segmentSignals(full_rec_final,list(r_peaks_final)) # Final Signal Segmentation\n\n    return seg_sigs # Returning the Ouput of the Signal Segmentation\n\n###### Function to Segment Signals\n\n##### Function\ndef segmentSignals(signal, r_peaks_annot, normalization=True, person_id= None, file_id=None):\n    \n    \"\"\"\n    Segments signals based on the detected R-Peak\n    Args:\n        signal (numpy array): input signal\n        r_peaks_annot (int []): r-peak locations.\n        normalization (bool, optional): apply z-normalization or not? . Defaults to True.\n        person_id ([type], optional): [description]. Defaults to None.\n        file_id ([type], optional): [description]. Defaults to None.\n    Returns:\n            [tuple(numpy array,numpy array)]: segmented signals and refined r-peaks\n    \"\"\"\n    def refine_rpeaks(signal, r_peaks):\n        \"\"\"\n        Refines the detected R-peaks. If the R-peak is slightly shifted, this assigns the \n        highest point R-peak.\n        Args:\n            signal (numpy array): input signal\n            r_peaks (int []): list of detected r-peaks\n        Returns:\n            [numpy array]: refined r-peaks\n        \"\"\"\n        r_peaks2 = np.array(r_peaks)            # make a copy\n        for i in range(len(r_peaks)):\n            r = r_peaks[i]          # current R-peak\n            small_segment = signal[max(0,r-100):min(len(signal),r+100)]         # consider the neighboring segment of R-peak\n            r_peaks2[i] = np.argmax(small_segment) - 100 + r_peaks[i]           # picking the highest point\n            r_peaks2[i] = min(r_peaks2[i],len(signal))                          # the detected R-peak shouldn't be outside the signal\n            r_peaks2[i] = max(r_peaks2[i],0)                                    # checking if it goes before zero    \n        return r_peaks2                     # returning the refined r-peak list\n    \n    segmented_signals = []                      # array containing the segmented beats\n    \n    r_peaks = np.array(r_peaks_annot)\n\n    r_peaks = refine_rpeaks(signal, r_peaks)\n    skip_len = 2 # Parameter to specify number of r_peaks in one signal\n    max_seq_len = 512 # Parameter to specify maximum sequence length\n    \n    for r_curr in range(0,int(r_peaks.shape[0]-(skip_len-1)),skip_len):\n        if ((r_peaks[r_curr]-W_LEN_1_4)<0) or ((r_peaks[r_curr+(skip_len-1)]+W_LEN_3_4)>=len(signal)):           # not enough signal to segment\n            continue\n        segmented_signal = np.array(signal[r_peaks[r_curr]-W_LEN_1_4:r_peaks[r_curr+(skip_len-1)]+W_LEN_3_4])        # segmenting a heartbeat\n        segmented_signal = list(segmented_signal)\n        #print(segmented_signal.shape)\n        \n        if(len(segmented_signal) < 512):\n            for m in range(int(512-len(segmented_signal))): # Zero Padding\n                segmented_signal.append(0)\n        else:\n            segmented_signal = (segmented_signal[:int(max_seq_len)])\n            \n        segmented_signal = np.array(segmented_signal)\n        \n        if(segmented_signal.shape != (512,1)):    \n            segmented_signal = np.reshape(segmented_signal,(512,1))\n            \n        if (normalization):             # Z-score normalization\n            if abs(np.std(segmented_signal))<1e-6:          # flat line ECG, will cause zero division error\n                continue\n            segmented_signal = (segmented_signal - np.mean(segmented_signal)) / np.std(segmented_signal)            \n              \n        #if not np.isnan(segmented_signal).any():                    # checking for nan, this will never happen\n            segmented_signals.append(segmented_signal)\n\n    return segmented_signals,r_peaks           # returning the segmented signals and the refined r-peaks","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"###### Extracting List of the Elements with Two Sessions \ndir = '../input/ecg1d/ecg-id-database-1.0.0'\ntotal_index = 0\nsubjects_with_two = []\n\nfor item in np.sort(os.listdir(dir)):\n    #print('----------------------------------')\n    #print(item)\n    dir_sub = os.path.join(dir,item)\n    if(os.path.isdir(dir_sub)):\n        #print(len(os.listdir(dir_sub))//3)\n        if(len(os.listdir(dir_sub))//3 == 2):\n            total_index = total_index+1   \n            #print(item)\n            subjects_with_two.append(item)\n    #print('----------------------------------')\n\nprint(total_index)\nprint(subjects_with_two)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"###### Numpy Array Creation\npath_to_dir = '../input/ecg1d/ecg-id-database-1.0.0'\ntotal_folders = 90\ncurrent_index = 0\n\nX_train = []\nX_dev = []\ny_train = []\ny_dev = []\n\n#for item in subjects_with_two:\nfor i in range(2,92):\n\n    if(i != 75):\n\n        print(i-1)\n        folder_path = os.path.join(path_to_dir,np.sort(os.listdir(path_to_dir))[i]) # Path Selection\n        #items_in_folder = int(len(folder_path)//3)\n        #current_storage_path = './5_Beat_Ecg_ECG1D'+'/person'+str(current_index)\n\n        #for j in os.listdir(item):\n\n        for j in range(2):\n\n            rec_path = folder_path+'/'+'rec'+'_'+str(j+1) # Path to Record\n            seg_signal_current = read_rec(rec_path)\n\n            if(j == 0):\n                for k in range(len(seg_signal_current)):\n                    #file_name_current = current_storage_path+'/'+str(j)+'_/'+str(k)\n                    #np.savez_compressed(file_name_current,seg_signal_current[k])\n                    X_train.append(seg_signal_current[k])\n                    y_train.append(current_index)\n\n            else:\n                for k in range(len(seg_signal_current)):\n                    #file_name_current = current_storage_path+'/'+str(j)+'_/'+str(k)\n                    #np.savez_compressed(file_name_current,seg_signal_current[k])\n                    X_dev.append(seg_signal_current[k])\n                    y_dev.append(current_index)\n\n        current_index = current_index+1\n\n###### Shuffling Numpy Arrays\nX_train,y_train = shuffle(X_train,y_train)\nX_dev,y_dev = shuffle(X_dev,y_dev)\n\n###### Saving Numpy Arrays\nnp.savez_compressed('X_train_ECG1D.npz',np.array(X_train))\nnp.savez_compressed('y_train_ECG1D.npz',np.array(y_train))\nnp.savez_compressed('X_dev_ECG1D.npz',np.array(X_dev))\nnp.savez_compressed('y_dev_ECG1D.npz',np.array(y_dev))  ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##### Loading Dataset - ECG-1D\nX_train = np.array(np.load('../input/ecg-1d-final-full/X_train_ECG1D_Full.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\nX_dev = np.array(np.load('../input/ecg-1d-final-full/X_dev_ECG1D_Full.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\ny_train = np.load('../input/ecg-1d-final-full/y_train_ECG1D_Full.npz',allow_pickle=True)['arr_0']\ny_dev = np.load('../input/ecg-1d-final-full/y_dev_ECG1D_Full.npz',allow_pickle=True)['arr_0']\n\n##### Loading Dataset - PTB\n#X_train = np.array(np.load('../input/ptb-290/X_train_PTB_290.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\n#X_dev = np.array(np.load('../input/ptb-290/X_dev_PTB_290.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\n#y_train = np.load('../input/ptb-290/y_train_PTB_290.npz',allow_pickle=True)['arr_0']\n#y_dev = np.load('../input/ptb-290/y_dev_PTB_290.npz',allow_pickle=True)['arr_0']\n\n##### Loading Dataset - MITBIH\n#X_train = np.array(np.load('../input/mitbih/X_train_Identification_MITBIH.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\n#X_dev = np.array(np.load('../input/mitbih/X_dev_Identification_MITBIH.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\n#y_train = np.load('../input/mitbih/y_train_Identification_MITBIH.npz',allow_pickle=True)['arr_0']\n#y_dev = np.load('../input/mitbih/y_dev_Identification_MITBIH.npz',allow_pickle=True)['arr_0']","metadata":{"execution":{"iopub.status.busy":"2021-07-23T14:03:04.537283Z","iopub.execute_input":"2021-07-23T14:03:04.537605Z","iopub.status.idle":"2021-07-23T14:03:04.927413Z","shell.execute_reply.started":"2021-07-23T14:03:04.537576Z","shell.execute_reply":"2021-07-23T14:03:04.926572Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"##### Converting Labels to Categorical Format\ny_train_ohot = tf.keras.utils.to_categorical(y_train)\ny_dev_ohot = tf.keras.utils.to_categorical(y_dev)","metadata":{"execution":{"iopub.status.busy":"2021-07-23T14:03:11.736967Z","iopub.execute_input":"2021-07-23T14:03:11.737280Z","iopub.status.idle":"2021-07-23T14:03:11.744838Z","shell.execute_reply.started":"2021-07-23T14:03:11.737251Z","shell.execute_reply":"2021-07-23T14:03:11.744024Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"# Model Making","metadata":{}},{"cell_type":"markdown","source":"## Self-Calibrated Convolution","metadata":{}},{"cell_type":"code","source":"###### Model Development : Self-Calibrated \n\n##### Defining Self-Calibrated Block\n\nrate_regularizer = 1e-5\nclass self_cal_Conv1D(tf.keras.layers.Layer):\n\n    \"\"\" \n    This is inherited class from keras.layers and shall be instatition of self-calibrated convolutions\n    \"\"\"\n    \n    def __init__(self,num_filters,kernel_size,num_features):\n    \n        #### Defining Essentials\n        super().__init__()\n        self.num_filters = num_filters\n        self.kernel_size = kernel_size\n        self.num_features = num_features # Number of Channels in Input\n\n        #### Defining Layers\n        self.conv2 = tf.keras.layers.Conv1D(self.num_features/2,self.kernel_size,padding='same',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32',activation='relu')\n        self.conv3 = tf.keras.layers.Conv1D(self.num_features/2,self.kernel_size,padding='same',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32',activation='relu')\n        self.conv4 = tf.keras.layers.Conv1D(self.num_filters/2,self.kernel_size,padding='same',activation='relu',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32')\n        self.conv1 = tf.keras.layers.Conv1D(self.num_filters/2,self.kernel_size,padding='same',activation='relu',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32')\n        self.upsample = tf.keras.layers.Conv1DTranspose(filters=int(self.num_features/2),kernel_size=4,strides=4)\n        #self.attention_layer = tf.keras.layers.Attention()\n        #self.lstm = tf.keras.layers.LSTM(int(self.num_features/2),return_sequences=True)\n        #self.layernorm = tf.keras.layers.LayerNormalization()\n    \n    def get_config(self):\n\n        config = super().get_config().copy()\n        config.update({\n            'num_filters': self.num_filters,\n            'kernel_size': self.kernel_size,\n            'num_features': self.num_features\n        })\n        return config\n    \n    \n    def call(self,X):\n       \n        \"\"\"\n          INPUTS : 1) X - Input Tensor of shape (batch_size,sequence_length,num_features)\n          OUTPUTS : 1) X - Output Tensor of shape (batch_size,sequence_length,num_features)\n        \"\"\"\n        \n        #### Dimension Extraction\n        b_s = (X.shape)[0] \n        seq_len = (X.shape)[1]\n        num_features = (X.shape)[2]\n        \n        #### Channel-Wise Division\n        X_attention = X[:,:,0:int(self.num_features/2)]\n        X_global = X[:,:,int(self.num_features/2):]\n        \n        #### Self Calibration Block\n\n        ### Local Feature Detection\n\n        ## Down-Sampling\n        #x1 = X_attention[:,0:int(seq_len/5),:]\n        #x2 = X_attention[:,int(seq_len/5):int(seq_len*(2/5)),:]\n        #x3 = X_attention[:,int(seq_len*(2/5)):int(seq_len*(3/5)),:]\n        #x4 = X_attention[:,int(seq_len*(3/5)):int(seq_len*(4/5)),:]\n        #x5 = X_attention[:,int(seq_len*(4/5)):seq_len,:]\n        x_down_sampled = tf.keras.layers.AveragePooling1D(pool_size=4,strides=4)(X_attention)\n        \n        ## Convoluting Down Sampled Sequence \n        #x1 = self.conv2(x1)\n        #x2 = self.conv2(x2)\n        #x3 = self.conv2(x3)\n        #x4 = self.conv2(x4)\n        #x5 = self.conv2(x5)\n        x_down_conv = self.conv2(x_down_sampled)\n        #x_down_feature = self.attention_layer([x_down_sampled,x_down_sampled])\n        #x_down_feature = self.lstm(x_down_sampled)\n        #x_down_feature = self.layernorm(x_down_feature)\n        \n        ## Up-Sampling\n        x_down_upsampled = self.upsample(x_down_conv)   \n        #X_local_upsampled = tf.keras.layers.concatenate([x1,x2,x3,x4,x5],axis=1)\n\n        ## Local-CAM\n        X_local = X_attention + x_down_upsampled  #X_local_upsampled\n\n        ## Local Importance \n        X_2 = tf.keras.activations.sigmoid(X_local)\n\n        ### Self-Calibration\n\n        ## Global Convolution\n        X_3 = self.conv3(X_attention)\n\n        ## Attention Determination\n        X_attention = tf.math.multiply(X_2,X_3)\n\n        #### Self-Calibration Feature Extraction\n        X_4 = self.conv4(X_attention)\n\n        #### Normal Feature Extraction\n        X_1 = self.conv1(X_global)\n\n        #### Concatenating and Returning Output\n        return (tf.keras.layers.concatenate([X_1,X_4],axis=2))","metadata":{"execution":{"iopub.status.busy":"2021-06-24T05:37:09.259664Z","iopub.execute_input":"2021-06-24T05:37:09.260089Z","iopub.status.idle":"2021-06-24T05:37:09.282987Z","shell.execute_reply.started":"2021-06-24T05:37:09.260054Z","shell.execute_reply":"2021-06-24T05:37:09.281238Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Transformer","metadata":{}},{"cell_type":"code","source":"def get_angles(pos, i, d_model):\n    angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))\n    return pos * angle_rates\n\ndef positional_encoding(position, d_model):\n    angle_rads = get_angles(np.arange(position)[:, np.newaxis],\n                          np.arange(d_model)[np.newaxis, :],\n                          d_model)\n  \n  # apply sin to even indices in the array; 2i\n    angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n  \n  # apply cos to odd indices in the array; 2i+1\n    angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n    \n    pos_encoding = angle_rads[np.newaxis, ...]\n    \n    return tf.cast(pos_encoding, dtype=tf.float32)\n\ndef create_padding_mask(seq):\n    seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\n  \n    # add extra dimensions to add the padding\n    # to the attention logits. \n    return seq[:, tf.newaxis, tf.newaxis, :]  # (batch_size, 1, 1, seq_len)\n\ndef scaled_dot_product_attention(q, k, v, mask):\n    \"\"\"Calculate the attention weights.\n    q, k, v must have matching leading dimensions.\n    k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v.\n    The mask has different shapes depending on its type(padding or look ahead) \n    but it must be broadcastable for addition.\n\n    Args:\n    q: query shape == (..., seq_len_q, depth)\n    k: key shape == (..., seq_len_k, depth)\n    v: value shape == (..., seq_len_v, depth_v)\n    mask: Float tensor with shape broadcastable \n          to (..., seq_len_q, seq_len_k). Defaults to None.\n\n    Returns:\n    output, attention_weights\n    \"\"\"\n\n    matmul_qk = tf.matmul(q, k, transpose_b=True)  # (..., seq_len_q, seq_len_k)\n  \n    # scale matmul_qk\n    dk = tf.cast(tf.shape(k)[-1], tf.float32)\n    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n\n    # add the mask to the scaled tensor.\n    if mask is not None:\n        scaled_attention_logits += (mask * -1e9)  \n\n    # softmax is normalized on the last axis (seq_len_k) so that the scores\n    # add up to 1.\n    attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)  # (..., seq_len_q, seq_len_k)\n\n    output = tf.matmul(attention_weights, v)  # (..., seq_len_q, depth_v)\n\n    return output, attention_weights\n\nclass MultiHeadAttention(tf.keras.layers.Layer):\n    def __init__(self, d_model, num_heads):\n        super(MultiHeadAttention, self).__init__()\n        self.num_heads = num_heads\n        self.d_model = d_model\n\n        assert d_model % self.num_heads == 0\n\n        self.depth = d_model // self.num_heads\n\n        self.wq = tf.keras.layers.Dense(d_model)\n        self.wk = tf.keras.layers.Dense(d_model)\n        self.wv = tf.keras.layers.Dense(d_model)\n\n        self.dense = tf.keras.layers.Dense(d_model)\n\n    def get_config(self):\n        config = super(MultiHeadAttention, self).get_config().copy()\n        config.update({\n            'd_model': self.d_model,\n            'num_heads':self.num_heads\n        })\n        \n    def split_heads(self, x, batch_size):\n        \n        \"\"\"Split the last dimension into (num_heads, depth).\n        Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)\n        \"\"\"\n        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))\n        return tf.transpose(x, perm=[0, 2, 1, 3])\n    \n    def call(self, v, k, q, mask):\n        batch_size = tf.shape(q)[0]\n\n        q = self.wq(q)  # (batch_size, seq_len, d_model)\n        k = self.wk(k)  # (batch_size, seq_len, d_model)\n        v = self.wv(v)  # (batch_size, seq_len, d_model)\n\n        q = self.split_heads(q, batch_size)  # (batch_size, num_heads, seq_len_q, depth)\n        k = self.split_heads(k, batch_size)  # (batch_size, num_heads, seq_len_k, depth)\n        v = self.split_heads(v, batch_size)  # (batch_size, num_heads, seq_len_v, depth)\n\n        # scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)\n        # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)\n        scaled_attention, attention_weights = scaled_dot_product_attention(\n            q, k, v, mask)\n\n        scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])  # (batch_size, seq_len_q, num_heads, depth)\n\n        concat_attention = tf.reshape(scaled_attention, \n                                      (batch_size, -1, self.d_model))  # (batch_size, seq_len_q, d_model)\n\n        output = self.dense(concat_attention)  # (batch_size, seq_len_q, d_model)\n\n        return output, attention_weights\n\ndef point_wise_feed_forward_network(d_model, dff):\n    return tf.keras.Sequential([\n      tf.keras.layers.Dense(dff, activation='relu'),  # (batch_size, seq_len, dff)\n      tf.keras.layers.Dense(d_model)  # (batch_size, seq_len, d_model)\n  ])\n\nclass Encoder(tf.keras.layers.Layer):\n    def __init__(self, num_layers, d_model, num_heads, dff,\n               maximum_position_encoding, rate=0.1):\n        super(Encoder, self).__init__()\n\n        self.d_model = d_model\n        self.num_layers = num_layers\n        self.num_heads = num_heads\n        self.dff = dff\n        self.maximum_position_encoding = maximum_position_encoding\n        self.rate = rate\n\n        #self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)\n        self.pos_encoding = positional_encoding(maximum_position_encoding, \n                                                self.d_model)\n\n\n        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate) \n                           for _ in range(num_layers)]\n\n        self.dropout = tf.keras.layers.Dropout(rate)\n        \n    def get_config(self):\n        config = super(Encoder, self).get_config().copy()\n        config.update({\n            'num_layers': self.num_layers,\n            'd_model': self.d_model,\n            'num_heads':self.num_heads,\n            'dff':self.dff,\n            'maximum_position_encoding':self.maximum_position_encoding,\n            'rate':self.rate  \n        })\n        \n    def call(self, x, training, mask):\n\n        seq_len = tf.shape(x)[1]\n\n        # adding embedding and position encoding.\n        #x = self.embedding(x)  # (batch_size, input_seq_len, d_model)\n        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n        x += self.pos_encoding[:, :seq_len, :]\n\n        x = self.dropout(x, training=training)         \n\n        for i in range(self.num_layers):\n            x = self.enc_layers[i](x, training, mask)\n\n        return x  # (batch_size, input_seq_len, d_model)\n\nclass EncoderLayer(tf.keras.layers.Layer):\n    def __init__(self, d_model, num_heads, dff, rate=0.1):\n        super(EncoderLayer, self).__init__()\n        \n        self.d_model = d_model\n        self.num_heads = num_heads\n        self.dff = dff\n        self.rate = rate\n\n        self.mha = MultiHeadAttention(d_model, num_heads)\n        self.ffn = point_wise_feed_forward_network(d_model, dff)\n\n        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n\n        self.dropout1 = tf.keras.layers.Dropout(rate)\n        self.dropout2 = tf.keras.layers.Dropout(rate)\n        \n    def get_config(self):\n        config = super(EncoderLayer, self).get_config().copy()\n        config.update({\n            'd_model': self.d_model,\n            'num_heads':self.num_heads,\n            'dff':self.dff,\n            'rate':self.rate  \n        })\n\n    def call(self, x, training, mask):\n\n        attn_output, _ = self.mha(x, x, x, mask)  # (batch_size, input_seq_len, d_model)\n        attn_output = self.dropout1(attn_output, training=training)\n        out1 = self.layernorm1(x + attn_output)  # (batch_size, input_seq_len, d_model)\n\n        ffn_output = self.ffn(out1)  # (batch_size, input_seq_len, d_model)\n        ffn_output = self.dropout2(ffn_output, training=training)\n        out2 = self.layernorm2(out1 + ffn_output)  # (batch_size, input_seq_len, d_model)\n    \n        return out2\n    \nclass Transformer(tf.keras.Model):\n    def __init__(self, num_layers, d_model, num_heads, dff, \n                 pe_input, rate=0.1):\n        super(Transformer, self).__init__()\n        \n        self.num_layers = num_layers\n        self.d_model = d_model\n        self.num_heads = num_heads\n        self.dff = dff\n        self.pe_input = pe_input\n        self.rate = rate\n        \n        self.encoder = Encoder(num_layers, d_model, num_heads, dff, \n                                pe_input, rate)\n        \n    def get_config(self):\n        config = super(Transformer,self).get_config().copy()\n        config.update({\n            'num_layers': self.num_layers,\n            'd_model': self.d_model,\n            'num_heads':self.num_heads,\n            'dff':self.dff,\n            'pe_input':self.pe_input,\n            'rate':self.rate  \n        })\n    \n    def call(self, inp, training, enc_padding_mask):\n        return self.encoder(inp, training, enc_padding_mask)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T05:37:12.185847Z","iopub.execute_input":"2021-06-24T05:37:12.186239Z","iopub.status.idle":"2021-06-24T05:37:12.231926Z","shell.execute_reply.started":"2021-06-24T05:37:12.186195Z","shell.execute_reply":"2021-06-24T05:37:12.230574Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## ArcFace Loss","metadata":{}},{"cell_type":"code","source":"###### ArcFace Layer\n\n##### Layer \nclass ArcFace(tf.keras.layers.Layer):\n\n    \"\"\" ArcFace Logits Generation Layer \"\"\"\n\n    def __init__(self,num_classes,s,m,input_embedding_dims):\n\n        #### Defining Essentials\n        super().__init__()\n        self.num_classes = num_classes # Number of Classes in the Outut\n        self.s = s # Geodesic Scaled Distance\n        self.m = m # Additive Angular Margin\n        self.input_embedding_dims = input_embedding_dims # Dimesnsion of Input to the ArcFace Layer\n\n        #### Defining Weight\n        self.W = self.add_weight(shape=(self.input_embedding_dims, self.num_classes),\n                                initializer='glorot_uniform',\n                                trainable=True,\n                                )\n\n    def get_config(self):\n\n        config = super().get_config().copy()\n        config.update({\n            'num_classes': self.num_classes,\n            's': self.s,\n            'm': self.m,\n            'input_embedding_dims': self.input_embedding_dims\n        })\n        return config\n\n    \n    def call(self,X):\n\n        #### Normalizing Layers\n        W = tf.nn.l2_normalize(self.W, axis=0)\n        X = tf.nn.l2_normalize(X, axis=1)\n\n        #### Logits Computation\n        logits = X @ W\n        #z = tf.linalg.matmul(X,W) #Logits\n\n        #### Additive Angular Margin \n        #theta = tf.acos(tf.keras.backend.clip(logits, -1.0 + tf.keras.backend.epsilon(), 1.0 - tf.keras.backend.epsilon())) #tf.math.acos(z)\n        #z = self.s*(tf.math.cos(theta+(self.m*y)))  # Final Logits\n\n        return logits  #tf.keras.layers.Softmax(axis=1)(z) # Returning Softmax Activated Result \n\n##### Loss \nclass ArcLoss(tf.keras.losses.Loss):\n\n    def __init__(self,num_classes,s,m):\n\n        #### Defining Essentials\n        super().__init__()\n        self.num_classes = num_classes # Number of Classes in the Outut\n        self.s = s # Geodesic Scaled Distance\n        self.m = m # Additive Angular Margin\n\n    def get_config(self):\n\n        config = super().get_config().copy()\n        config.update({\n            'num_classes': self.num_classes,\n            's': self.s,\n            'm': self.m\n        })\n        return config\n\n    def call(self,y_true,logits):\n        \n        theta = tf.acos(tf.keras.backend.clip(logits, -1.0 + tf.keras.backend.epsilon(), 1.0 - tf.keras.backend.epsilon()))\n        target_logits = tf.cos(theta + self.m)\n        # sin = tf.sqrt(1 - logits**2)\n        # cos_m = tf.cos(logits)\n        # sin_m = tf.sin(logits)\n        # target_logits = logits * cos_m - sin * sin_m\n        #\n        logits = logits * (1 - y_true) + target_logits * y_true\n        # feature re-scale\n        logits *= self.s\n        out = tf.nn.softmax(logits)    \n        \n        return tf.keras.losses.categorical_crossentropy(y_true,out)\n\n        #z = self.s*(tf.math.cos(theta+(self.m*y_true)))\n        #final_logits = tf.keras.layers.Softmax(axis=1)(z) # Softmax Operated Final Logits\n        #return ","metadata":{"execution":{"iopub.status.busy":"2021-06-24T05:37:25.890568Z","iopub.execute_input":"2021-06-24T05:37:25.89098Z","iopub.status.idle":"2021-06-24T05:37:25.906487Z","shell.execute_reply.started":"2021-06-24T05:37:25.890947Z","shell.execute_reply":"2021-06-24T05:37:25.904979Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model Training","metadata":{}},{"cell_type":"code","source":"####### Phase-1 Models\n###### Defining Architecture\n\nwith tpu_strategy.scope():\n\n    ##### SC_Module \n\n    #### Defining Hyperparameters\n    num_layers = 2\n    d_model = 512\n    num_heads = 8\n    dff = 1024\n    max_seq_len = 512 #X_train.shape[1]\n    pe_input = 128\n    rate = 0.5\n    num_features = 1\n    num_classes = 47\n\n    #### Defining Layers\n    Input_layer = tf.keras.layers.Input(shape=(max_seq_len,num_features))\n    self_conv1 = self_cal_Conv1D(128,15,128)\n    self_conv2 = self_cal_Conv1D(128,20,128) # Newly Added\n    self_conv3 = self_cal_Conv1D(256,15,128)\n    self_conv4 = self_cal_Conv1D(256,20,256) # Newly Added\n    self_conv5 = self_cal_Conv1D(512,15,256)\n    self_conv6 = self_cal_Conv1D(512,20,512) # Newly Added\n    self_conv7 = self_cal_Conv1D(1024,3,512)\n    self_conv8 = self_cal_Conv1D(1024,5,1024) # Newly Added\n    conv_initial = tf.keras.layers.Conv1D(32,15,padding='same',activation='relu')\n    conv_second = tf.keras.layers.Conv1D(64,15,padding='same',activation='relu')\n    conv_third = tf.keras.layers.Conv1D(128,15,padding='same',activation='relu')\n    #lstm1 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128,activation='tanh',return_sequences=True),merge_mode='ave')\n    transform_1 = tf.keras.layers.Conv1D(128,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transform_2 = tf.keras.layers.Conv1D(256,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transform_3 = tf.keras.layers.Conv1D(512,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transform_4 = tf.keras.layers.Conv1D(1024,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transformer = Transformer(num_layers,d_model,num_heads,dff,pe_input,rate)\n    gap_layer = tf.keras.layers.GlobalAveragePooling1D()\n    ArcFace_Layer = ArcFace(47,30.0,0.3,256)\n    ArcFace_Loss = ArcLoss(47,30.0,0.3)\n    #arc_logit_layer = ArcFace(89,30.0,0.3,tf.keras.regularizers.l2(1e-4))\n\n    #### Defining Architecture\n    ### Input Layer\n    Inputs = Input_layer\n    #Input_Labels = tf.keras.layers.Input(shape=(num_classes,))\n\n    ### Initial Convolutional Layers\n    conv_initial = conv_initial(Inputs)\n    #conv_initial = tf.keras.layers.LayerNormalization()(conv_initial)\n    #conv_initial = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv_initial)     \n    #conv_initial = tf.keras.layers.Add()([conv_initial,Inputs])\n    \n    conv_second = conv_second(conv_initial)\n    #conv_second = tf.keras.layers.LayerNormalization()(conv_second)\n    #conv_second = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv_second)\n    #conv_second = tf.keras.layers.Add()([conv_second,conv_initial])\n    #conv_second = tf.keras.layers.concatenate(axis=2)([conv_initial,conv_second])\n    \n    conv_third = conv_third(conv_second)\n    #conv_third = tf.keras.layers.LayerNormalization()(conv_third)\n    #conv_third = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv_third)\n    #mask = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(Inputs)\n    #conv_third = tf.keras.layers.Add()([conv_third,conv_second])\n    #conv_third = tf.keras.layers.concatenate(axis=2)([conv_initial,conv_second,conv_third])\n    #conv_third = lstm1(conv_second)\n    #conv_third = tf.keras.layers.Attention()([conv_third,conv_third])\n    \n    ### 1st Residual Block\n    transform_1 = transform_1(conv_third)\n    conv1 = self_conv1(conv_third)\n    #conv1 = tf.keras.layers.AlphaDropout(rate=0.2)(conv1)\n    conv2 = self_conv2(conv1)\n    #conv2 = tf.keras.layers.AlphaDropout(rate=0.2)(conv2)\n    conv2 = tf.keras.layers.Add()([conv2,transform_1])\n    #conv2 = tf.keras.layers.LayerNormalization()(conv2)\n    conv2 = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv2)\n    #mask = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(mask)    \n\n    ### 2nd Residual Block\n    #conv_third = tf.keras.layers.Attention()([conv_third,conv_third])\n    transform_2 = transform_2(conv2)\n    conv3 = self_conv3(conv2)\n    #conv3 = tf.keras.layers.AlphaDropout(rate=0.2)(conv3)\n    conv4 = self_conv4(conv3)\n    #conv4 = tf.keras.layers.AlphaDropout(rate=0.2)(conv4)\n    conv4 = tf.keras.layers.Add()([conv4,transform_2])\n    #conv4 = tf.keras.layers.LayerNormalization()(conv4)\n    conv4 = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv4)\n    #mask = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(mask)\n\n    ### 3rd Residual Block\n    transform_3 = transform_3(conv4)\n    conv5 = self_conv5(conv4)\n    #conv5 = tf.keras.layers.AlphaDropout(rate=0.2)(conv5)\n    conv6 = self_conv6(conv5)\n    #conv6 = tf.keras.layers.AlphaDropout(rate=0.2)(conv6)\n    conv6 = tf.keras.layers.Add()([conv6,transform_3])\n    #conv6 = tf.keras.layers.LayerNormalization()(conv6)\n    #conv6 = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv6)\n\n    ### 4th Residual Block\n    #transform_4 = transform_4(conv6)\n    #conv7 = self_conv7(conv6)\n    #conv8 = self_conv8(conv7)\n    #conv8 = tf.keras.layers.Add()([conv8,transform_4])\n\n    ### Transformer\n    ## Wide-Head Attention Model\n    #tx_embedding = tf.keras.layers.Lambda(PE_Layer)(Inputs)\n    #tx_embedding = tf.keras.layers.Dropout(rate)(tx_embedding,training=True)\n    #mask_reshaped = tf.keras.layers.Reshape((max_seq_len,))(Inputs)\n    #encoder_op1 = encoder_block1(tx_embedding,mask_reshaped)\n    #encoder_op2 = encoder_block2(encoder_op1,mask_reshaped)\n\n    ## Narrow-Head Attention Model\n    #mask_reshaped = tf.keras.layers.Reshape((160,))(mask)\n    embeddings =  transformer(inp=conv6,enc_padding_mask=None)\n    #embeddings = transformer(inp=conv6,enc_padding_mask=create_padding_mask(mask))\n    #residual_embeddings = tf.keras.layers.Add()([conv6,embeddings])\n\n    ### Output Layers\n    ## Initial Layers\n    gap_op = gap_layer(embeddings)\n    dense1 = tf.keras.layers.Dense(256,activation='relu')(gap_op)\n    dropout1 = tf.keras.layers.Dropout(rate)(dense1)\n    \n    ## ArcFace Output Network\n    dense2 = tf.keras.layers.Dense(256,kernel_initializer='he_normal',\n                kernel_regularizer=tf.keras.regularizers.l2(1e-4))(dropout1)\n    dense3 = ArcFace_Layer(dense2)\n    ##dense2 = tf.keras.layers.BatchNormalization()(dense2)\n    #dense3 = arc_logit_layer(([dense2,Input_Labels]))\n    \n    ## Softmax Output Network\n    #dense2 = tf.keras.layers.Dense(256,activation='relu')(dropout1)\n    ###dropout2 = tf.keras.layers.Dropout(rate)(dense2) # Not to be included\n    #dense3 = tf.keras.layers.Dense(35,activation='softmax')(dense2)\n\n    #### Compiling Architecture            \n    ### ArcFace Model Compilation\n    model = tf.keras.models.Model(inputs=Inputs,outputs=dense3)\n    ### Softmax Model Compilation\n    #model = tf.keras.models.Model(inputs=Inputs,outputs=dense3)\n    \n    #model.load_weights('../input/ecg1d-models/Identification_ECG1D.h5')\n    model = tf.keras.models.Model(inputs=Inputs,outputs=dense3)\n    model.load_weights('./Identification_ECG1D_Incremental-MITBIH.h5')\n    model.compile(optimizer=tf.keras.optimizers.Adam(lr=1e-4,clipnorm=1.0),loss=ArcFace_Loss,metrics=['accuracy'])\n\nmodel.summary()      \ntf.keras.utils.plot_model(model)\n##### Model Training \n\n#### Model Checkpointing\nfilepath = './Identification_ECG1D_Incremental-MITBIH.h5'\ncheckpoint = tf.keras.callbacks.ModelCheckpoint(filepath,monitor='val_accuracy',save_best_only=True,mode='max',save_weights_only=True)\n\n#### Model Training\n#model.fit(X_train,y_train_ohot,epochs=250,batch_size=128,\n#          validation_data=(X_dev,y_dev_ohot),validation_batch_size=128,\n#         callbacks=checkpoint)\n\n##### Plotting Metrics  \n#### Accuracy and Loss Plots \n\n### Accuracy\n#plt.plot(history.history['accuracy'])\n#plt.plot(history.history['val_accuracy'])\n#plt.title('Model Accuracy')\n#plt.ylabel('Accuracy')\n#plt.xlabel('Epoch')  \n#plt.legend(['Train', 'Validation'], loc='best')\n#plt.show()\n\n### Loss     \n#plt.plot(history.history['loss'])  \n#plt.plot(history.history['val_loss'])\n#plt.title('Model Loss')  \n#plt.ylabel('Loss')         \n#plt.xlabel('epoch')\n#plt.legend(['Train', 'Validation'], loc='best')   \n#plt.show()","metadata":{"execution":{"iopub.status.busy":"2021-06-24T06:32:43.248844Z","iopub.execute_input":"2021-06-24T06:32:43.249267Z","iopub.status.idle":"2021-06-24T06:32:47.165041Z","shell.execute_reply.started":"2021-06-24T06:32:43.249234Z","shell.execute_reply":"2021-06-24T06:32:47.16387Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Incremental Learning","metadata":{}},{"cell_type":"code","source":"###### Base Model \n\nwith tpu_strategy.scope():\n    predictive_model = tf.keras.models.Model(inputs=model.input,outputs=model.layers[-2].output)\n    predictive_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n\n###### Incremental Learning Model\n\nwith tpu_strategy.scope():\n    \n    ArcFace_Layer = ArcFace(89,30.0,0.3,256)\n    ArcFace_Loss = ArcLoss(89,30.0,0.3)\n    \n    Input_Layer = tf.keras.layers.Input((512,1))\n    op_preds = predictive_model(Input_Layer)\n    final_logits = ArcFace_Layer(op_preds)\n\n    testing_model = tf.keras.models.Model(inputs=Input_Layer,outputs=final_logits)\n    testing_model.load_weights('./Identification_ECG1D_Incremental_Final.h5')\n    testing_model.compile(optimizer=tf.keras.optimizers.Adam(lr=1e-4,clipnorm=1.0),loss=ArcFace_Loss,metrics=['accuracy'])\n\ntesting_model.summary()      \ntf.keras.utils.plot_model(testing_model)\n\n#### Model Checkpointing\nfilepath = './Identification_ECG1D_Incremental_Final.h5'\ncheckpoint = tf.keras.callbacks.ModelCheckpoint(filepath,monitor='val_accuracy',save_best_only=True,mode='max',save_weights_only=True)\n\n#### Model Training\n#testing_model.fit(X_train,y_train_ohot,epochs=500,batch_size=128,\n#          validation_data=(X_dev,y_dev_ohot),validation_batch_size=128,\n#        callbacks=checkpoint)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T06:43:33.744375Z","iopub.execute_input":"2021-06-24T06:43:33.745043Z","iopub.status.idle":"2021-06-24T06:43:36.21606Z","shell.execute_reply.started":"2021-06-24T06:43:33.744986Z","shell.execute_reply":"2021-06-24T06:43:36.214822Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Model Testing","metadata":{}},{"cell_type":"markdown","source":"## KNN Testing Based ArcFace Loss","metadata":{}},{"cell_type":"code","source":"###### Testing Model - ArcFace Style\nwith tpu_strategy.scope():     \n\n    def normalisation_layer(x):   \n        return(tf.math.l2_normalize(x, axis=1, epsilon=1e-12))\n\n    #X_dev_flipped = tf.image.flip_up_down(X_dev)  \n    #x_train_flipped = tf.image.flip_up_down(X_train_final)\n\n    predictive_model = tf.keras.models.Model(inputs=model.input,outputs=model.layers[-3].output)\n    predictive_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n\nwith tpu_strategy.scope():\n    #y_in = tf.keras.layers.Input((89,))\n\n    Input_Layer = tf.keras.layers.Input((512,1))\n    op_1 = predictive_model(Input_Layer)\n\n    ##Input_Layer_Flipped = tf.keras.layers.Input((224,224,3))\n    ##op_2 = predictive_model([Input_Layer_Flipped,y_in]) \n    ##final_op = tf.keras.layers.Concatenate(axis=1)(op_1)\n\n    final_norm_op = tf.keras.layers.Lambda(normalisation_layer)(op_1)\n\n    testing_model = tf.keras.models.Model(inputs=Input_Layer,outputs=final_norm_op)\n    testing_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n\n##### Nearest Neighbor Classification \nfrom sklearn.neighbors import KNeighborsClassifier\nTest_Embeddings = testing_model.predict(X_dev)\nTrain_Embeddings = testing_model.predict(X_train)\n\ncol_mean = np.nanmean(Test_Embeddings, axis=0)\ninds = np.where(np.isnan(Test_Embeddings))\n#print(inds)\nTest_Embeddings[inds] = np.take(col_mean, inds[1])\n\ncol_mean = np.nanmean(Train_Embeddings, axis=0)\ninds = np.where(np.isnan(Train_Embeddings))\n#print(inds)\nTrain_Embeddings[inds] = np.take(col_mean, inds[1])\n\n#Test_Embeddings = np.nan_to_num(Test_Embeddings)\n\n##### Refining Test Embeddings\n#for i in range(Train_Embeddings.shape[0]):\n#    for j in range(Train_Embeddings.shape[1]):\n#        if(math.isnan(Train_Embeddings[i,j])):\n#            Train_Embeddings[i,j] == 0\n#        if(Train_Embeddings[i,j]>1e4):\n#            Train_Embeddings[i,j] == 1e4\n\n##### Refining Train Embeddings    \n#for i in range(Test_Embeddings.shape[0]):\n#    for j in range(Test_Embeddings.shape[1]):\n#        if(math.isnan(Test_Embeddings[i,j])):\n#            Test_Embeddings[i,j] == 0\n#        if(Test_Embeddings[i,j]>1e4 or math.isinf(Test_Embeddings[i,j])):\n#            Test_Embeddings[i,j] == 1e4\n\n#del(X_train_final,X_dev,X_dev_flipped,x_train_flipped)\n#gc.collect()\n\nTest_Accuracy_With_Train = []\nTest_Accuracy_With_Test = []\n                                                                     \nfor k in range(1,11):\n    knn = KNeighborsClassifier(n_neighbors=k,metric='euclidean')\n    knn.fit(Train_Embeddings,y_train)\n    Test_Accuracy_With_Train.append(knn.score(Test_Embeddings,y_dev))\n    knn.fit(Test_Embeddings,y_dev)\n    Test_Accuracy_With_Test.append(knn.score(Test_Embeddings,y_dev))\n\nprint('--------------------------------')\nprint(np.max(Test_Accuracy_With_Train))\nprint(np.max(Test_Accuracy_With_Test))\nprint('--------------------------------')\nprint(np.mean(Test_Accuracy_With_Train))\nprint(np.mean(Test_Accuracy_With_Test))\nprint('--------------------------------')\nprint((Test_Accuracy_With_Train)[0])\nprint((Test_Accuracy_With_Test)[0])\nprint('--------------------------------')\n\nplt.plot(np.arange(1,11),np.array(Test_Accuracy_With_Train),label='Test_Accuracy_With_Train')\nplt.plot(np.arange(1,11),np.array(Test_Accuracy_With_Test),label='Test_Accuracy_With_Test')\nplt.title('Testing Accuracy vs Number of Neighbors')\nplt.xlabel('Number of Neighbors')\nplt.ylabel('Test Accuracy')\nplt.legend()       \nplt.show()  \n\nnp.savez_compressed('TesEmb_ECG1D_MITBIH.npz',Test_Embeddings)\nnp.savez_compressed('TrainEmb_ECG1D_MITBIH.npz',Train_Embeddings)","metadata":{"execution":{"iopub.status.busy":"2021-06-24T06:45:19.503768Z","iopub.execute_input":"2021-06-24T06:45:19.504175Z","iopub.status.idle":"2021-06-24T06:45:30.090977Z","shell.execute_reply.started":"2021-06-24T06:45:19.504142Z","shell.execute_reply":"2021-06-24T06:45:30.09007Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## t-SNE ","metadata":{}},{"cell_type":"code","source":"####### t-SNE Plot Generation\n###### Model Creation\n#with tpu_strategy.scope():          \n#    tsne_model = tf.keras.models.Model(inputs=model.input,outputs=model.layers[-4].output)\n#    tsne_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n#tsne_model.summary()\n\n###### Model Predicted\n#embeddings_final = tsne_model.predict((X_dev,y_exp))\n\n###### t-SNE plot plotting\n##### Reduction to Lower Dimensions\ntsne_X_dev = TSNE(n_components=2,perplexity=30,learning_rate=10,n_iter=2000,n_iter_without_progress=50).fit_transform(Test_Embeddings)\n\n##### Plotting\nj = 0 # Index for rotating legend\nplt.rcParams[\"figure.figsize\"] = [12,8]\nmStyles = [\".\",\",\",\"o\",\"v\",\"^\",\"<\",\">\",\"1\",\"2\",\"3\",\"4\",\"8\",\"s\",\"p\",\"P\",\"*\",\"h\",\"H\",\"+\",\"x\",\"X\",\"D\",\"d\",\"|\",\"_\",0,1,2,3,4,5,6,7,8,9,10,11,0,1,2,3,4,5,6,7,8,9,10]\nfor idx,color_index,marker_type in zip(list(np.arange(89)),sns.color_palette('muted',47),mStyles):\n    plt.scatter(tsne_X_dev[y_dev == idx, 0], tsne_X_dev[y_dev == idx, 1],marker=marker_type)\n#plt.legend([str(j) for j in range(89)])\nplt.savefig('tsne_plot_5000_iters.png')\nplt.savefig('tsne_plot_5000_iters.pdf')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-06-24T06:46:38.639193Z","iopub.execute_input":"2021-06-24T06:46:38.64003Z","iopub.status.idle":"2021-06-24T06:46:47.882127Z","shell.execute_reply.started":"2021-06-24T06:46:38.639961Z","shell.execute_reply":"2021-06-24T06:46:47.88101Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Error Analysis","metadata":{}},{"cell_type":"markdown","source":"## Plotting Outliers","metadata":{}},{"cell_type":"code","source":"###### Function to Retrieve an Example\n\ndef retrieve_example(cl_ind,cl_item_ind,cl_type):\n\n    \"\"\" \n    Function to retrieve a particular example indexed according to the STS Matching File\n\n    INPUTS:-\n    1)cl_ind : Class Index of the example\n    2)cl_item_ind : Index of the example amongst the class vector\n    3)cl_type : String flag between gal and probe\n\n    OUTPUTS:-\n    1)ret_example : The item shaped [sequence_len,1], that is to be retrieved\n\n    \"\"\"\n\n    ##### For Gallery's Example\n    \n    if(cl_type=='gal'):\n\n        #### Setting the Correct Gallery Class and ID\n        gal_class = cl_ind-1 # gal_class\n        gal_id = cl_item_ind - 1 # gal_id\n \n        #### Searching the required Example - Curating Examples from gal_class\n        item_index = []\n        for j in range(X_train.shape[0]):\n            if(y_train[j] == gal_class):\n                item_index.append(j)\n\n        ret_example = X_train[item_index[int(gal_id)]] # Retrieving the required\n\n    ##### For Probe Class\n\n    if(cl_type=='probe'):\n        \n        #### Setting the Correct Probe Class and ID\n        probe_class = cl_ind - 1 # probe_class\n        \n        gallery_index = []\n        for j in range(X_train.shape[0]):\n            if(y_train[j] == probe_class):\n                gallery_index.append(j)  \n\n        gal_len = len(gallery_index) \n\n        probe_id = cl_item_ind - gal_len - 1 # probe_id\n\n    #### Searching the required Example - Curating Examples from gal_class\n        item_index = []\n        for j in range(X_dev.shape[0]):\n            if(y_dev[j] == probe_class):\n                item_index.append(j) \n\n        ret_example = X_dev[item_index[int(probe_id)]] # Retrieving the required\n\n    ##### Returning the Example\n\n    return ret_example","metadata":{"execution":{"iopub.status.busy":"2021-07-23T14:03:25.329212Z","iopub.execute_input":"2021-07-23T14:03:25.329547Z","iopub.status.idle":"2021-07-23T14:03:25.339280Z","shell.execute_reply.started":"2021-07-23T14:03:25.329518Z","shell.execute_reply":"2021-07-23T14:03:25.338271Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"###### Making the Plot Storing Directory\n! mkdir './Outlier Plots'","metadata":{"execution":{"iopub.status.busy":"2021-07-20T08:09:18.798264Z","iopub.execute_input":"2021-07-20T08:09:18.798776Z","iopub.status.idle":"2021-07-20T08:09:19.550609Z","shell.execute_reply.started":"2021-07-20T08:09:18.798743Z","shell.execute_reply":"2021-07-20T08:09:19.548441Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"###### Plotting the Outliers\n\n##### Fetching the Examples\n\nitem_1 = retrieve_example(34,20,'probe') \nitem_2 = retrieve_example(60,6,'gal')\nitem_3 = retrieve_example(85,8,'gal')\n\n##### Plotting the Requires \n\nplt.rcParams[\"figure.figsize\"] = [16,8]\nplt.plot(np.arange(512),item_1)\nplt.plot(np.arange(512),item_2)\nplt.plot(np.arange(512),item_3)\nplt.xlabel('T')\nplt.ylabel('V')\nplt.title('Ratio : '+str(6))\nplt.legend([str(j) for j in range(3)])\n#plt.savefig('./Outlier Plots/OP_5.png')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-07-23T14:03:33.648200Z","iopub.execute_input":"2021-07-23T14:03:33.648692Z","iopub.status.idle":"2021-07-23T14:03:34.028240Z","shell.execute_reply.started":"2021-07-23T14:03:33.648662Z","shell.execute_reply":"2021-07-23T14:03:34.027106Z"},"trusted":true},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA64AAAHwCAYAAACmHTLcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/Z1A+gAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdd5xdBZ3//9e5vUzLlPROCCQk9N4EBARFRFAUUVT46lp217WsZdfVtaxrX92fa0GxA4ogoiK9SguEll5IMkkmbXq7vZzfH2fOnZnMvTP33rkzd+bm/Xw8eITceiaZ3Dmf82mGaZqIiIiIiIiITFWOch+AiIiIiIiIyGgUuIqIiIiIiMiUpsBVREREREREpjQFriIiIiIiIjKlKXAVERERERGRKU2Bq4iIiIiIiExpClxFRESmCMMwfmwYxn+U+zhERESmGgWuIiIiRTIMo9kwjIhhGP2GYRw0DOOXhmFU5fnc9xmG8dTQ20zT/JBpml+ZoGMNGIbxQ8Mw2g3D6DEM48mJeB8REZGJoMBVRERkfN5smmYVcCJwEvC58h5OTjcD9cCKgV8/Xt7DERERyZ8CVxERkRIwTfMg8ABWAAuAYRifNQxjh2EYfYZhbDIM460Dt68AfgycNZCt7R64/ZeGYXx1yPM/YBjGa4ZhdBqG8WfDMOYWc2yGYRwLXAl80DTNNtM0U6Zpvljs1yoiIjLZFLiKiIiUgGEY84HLgdeG3LwDOA+oBb4E/NYwjDmmaW4GPgQ8a5pmlWmadVle7yLgv4FrgTnAbuB3o7z/OsMw3pXj7tMHnv+lgVLh9YZhXFPo1ygiIlIuClxFRETG50+GYfQBe4FW4Iv2HaZp/sE0zf2maaZN0/w9sB0riMzH9cDPTdN8yTTNGFYJ8lmGYSzO9mDTNI83TfO2HK81H1gF9ABzgX8EfjWQ+RUREZnyFLiKiIiMz1WmaVYDFwDHAo32HYZh3GAYxiuGYXQPlAOvGnr/GOZiZUkBME2zH+gA5hVxjBEgAXzVNM24aZpPAI8BlxbxWiIiIpNOgauIiEgJDASDvwS+DWAYxiLgp1jZzYaBcuANgGE/ZYyX3A8ssn9jGEYQaAD2FXF467IdchGvIyIiUhYKXEVERErne8AlhmGcAASxgsM2AMMw3o+VcbUdAuYbhuHJ8Vq3A+83DONEwzC8wNeANaZpNhdxXE8Ce4DPGYbhMgzjHOBCrGFSIiIiU54CVxERkRIxTbMN+DXwBdM0NwHfAZ7FClJXA08PefijwEbgoGEY7Vle62HgP4C7gAPAUcA7c723YRgbDcO4PsdxJYC3AG/E6nP9KXCDaZpbCv0aRUREysEwTVUKiYiIiIiIyNSljKuIiIiIiIhMaQpcRUREREREZEpT4CoiIiIiIiJTmgJXERERERERmdIUuIqIiIiIiMiU5ir3ARSisbHRXLx4cbkPQ0RERERERCbAiy++2G6aZtPht0+rwHXx4sWsXbu23IchIiIiIiIiE8AwjN3ZblepsIiIiIiIiExpClxFRERERERkSlPgKiIiIiIiIlPatOpxFRERERERkdwSiQQtLS1Eo9FyH8qofD4f8+fPx+125/V4Ba4iIiIiIiIVoqWlherqahYvXoxhGOU+nKxM06Sjo4OWlhaWLFmS13NUKiwiIiIiIlIhotEoDQ0NUzZoBTAMg4aGhoKywgpcRUREREREKshUDlpthR6jAlcREREREREpqfvvv59jjjmGZcuW8fWvf33cr6fAVUREREREREomlUrx0Y9+lPvuu49NmzZx++23s2nTpnG9pgJXERERERERKZnnn3+eZcuWsXTpUjweD+985zu55557xvWamiosIiIiIiJSgb70l41s2t9b0tdcObeGL775uFEfs2/fPhYsWJD5/fz581mzZs243lcZVxEREREREZnSlHEVERERERGpQGNlRifKvHnz2Lt3b+b3LS0tzJs3b1yvWdaMq2EYzYZhrDcM4xXDMNaW81hERERERERk/E477TS2b9/Orl27iMfj/O53v+PKK68c12tOhYzrhaZptpf7IERERERERGT8XC4XP/jBD3jDG95AKpXixhtv5Ljjxpf9nQqBq4iIiIiIiFSQN77xjbzxjW8s2euVeziTCTxoGMaLhmF8MNsDDMP4oGEYaw3DWNvW1jbJhyeVIJU2y30IIiIiIiIyDuUOXM81TfNk4HLgo4ZhnH/4A0zTvNk0zVNN0zy1qalp8o9QprWbn9zBOV9/lK0H+8p9KCIiIiIiUqSyBq6mae4b+LUVuBs4vZzHI5Vn+6F+DvZGecfNzyp4FRERERGZpsoWuBqGETQMo9r+f+BSYEO5jkcqU280wewaH6FYkj++3FLuwxERERERkSKUczjTLOBuwzDs47jNNM37y3g8UoF6I0kW1PtJpNL0R5PlPhwRERERESlC2QJX0zR3AieU6/3lyGBnXINeF6GYAlcRERERkemo3MOZRCZUbzRBjd9N0OuiX4GriIiIiMikuPHGG5k5cyarVq0qyespcJWK1htJUuNzUeV1KnAVEREREZkk73vf+7j//tJ1gipwlYplmiZ90QTVPvdAqXCq3IckIiIiInJEOP/886mvry/Z65VzOJPIhArFU6RNqPG7CHpd7OkIl/uQREREREQmz32fhYPrS/uas1fD5V8v7WvmQRlXqVi9kQQANT43VR71uIqIiIiITFfKuErF6o0OBK5+t6YKi4iIiMiRpwyZ0YmijKtUrN6IFajW+NxUeZ1W6XDaLPNRiYiIiIhIoRS4SsXKlAoP9LgChBMa0CQiIiIiMtGuu+46zjrrLLZu3cr8+fO55ZZbxvV6KhWWipUpFR6YKgwQiiWp8urbXkRERERkIt1+++0lfT1lXKViDWZc3ZlgVQOaRERERESmHwWuUrF6o1aQWu1zDcu4ioiIiIjI9KLAVSpWbySB3+3E7XQQ9DoBZVxFRERERKYjBa5SsfqiSWr8Vqa1KpNx1XAmEREREZHpRoGrVKzeaIIanxtApcIiIiIiItOYAlepWL3RBDV+K3DVcCYRERERkelLgatUrN5Ikhrf4aXCClxFRERERCbS3r17ufDCC1m5ciXHHXcc3//+98f9mlpoKRWrN5pgaVMQgIDHiWEocBURERERmWgul4vvfOc7nHzyyfT19XHKKadwySWXsHLlyqJfUxlXqVi9kcEeV8MwCHpc9Gs4k4iIiIjIhJozZw4nn3wyANXV1axYsYJ9+/aN6zWVcZWKZJomvUOmCgMEvU5lXEVERETkiPGN57/Bls4tJX3NY+uP5TOnfybvxzc3N/Pyyy9zxhlnjOt9lXGVihSOp0ilzUzGFazJwv1xBa4iIiIiIpOhv7+fa665hu9973vU1NSM67WUcZWK1BtNAFA9JHCt8rqUcRURERGRI0YhmdFSSyQSXHPNNVx//fVcffXV4349ZVylIvVHrQC1yjekVNijwFVEREREZKKZpslNN93EihUr+MQnPlGS11TgKhUplkwD4HMNfosHvRrOJCIiIiIy0Z5++ml+85vf8Oijj3LiiSdy4okn8re//W1cr6lSYalIsaQVoHrdzsxtVRrOJCIiIiIy4c4991xM0yzpayrjKhUpmsiecVXgKiIiIiIy/ShwlYqUPePqol+Bq4iIiIjItKPAVSpSbCDj6j0s4xpLpkmm0uU6LBERERERKYICV6lI0YGMq29IxjXotVq6QxrQJCIiIiIVrNT9pROh0GNU4CoVKWvG1WMFsaG4yoVFREREpDL5fD46OjqmdPBqmiYdHR34fL68n6OpwlKR7HU4QwNXO/tq3yciIiIiUmnmz59PS0sLbW1t5T6UUfl8PubPn5/34xW4SkWKZSkV9rmtIDaaUKmwiIiIiFQmt9vNkiVLyn0YJadSYalI0Sylwl6XMq4iIiIiItORAlepSLFkCqfDwOUcGrha/x9TxlVEREREZFpR4CoVKZZID8u2wuBO16gyriIiIiIi04oCV6lI0WRqWH8rKOMqIiIiIjJdKXCVipQt4+pTxlVEREREZFpS4CoVKZbMUiqsjKuIiIiIyLSkwFUqUixbqfDAOhxNFRYRmTj377qfSDJS7sMQEZEKo8BVKlJ0tFJhZVxFRCbEzp6d/OuT/8pfdvyl3IciIiIVRoGrVKRYMpXZ22rLlAor4yoiMiFa+loA2Na1rcxHIiIilUaBq1SkWDKdKQ22eZwODEOBq4jIRNnfvx+AHd07ynwkIiJSaRS4SkWySoWHZ1wNw8Drcmg4k4jIBFHgKiIiE0WBq1SkWDI1IuMK4HU5lXEVEZkg+/r3AdAV66Ij0lHmoxERkUqiwFUqUrY9rmD1uWo4k4jIxNjfvx+f0wdMbNa1N5qgJ5yYsNcXEZGpR4GrVKRs63DAmiysjKuIyMTYH9rP6XNOB+C17tcm7H0+e9c6PvibtRP2+iIiMvUocJWKNFrGNZZUxlVEpNQiyQid0U5OaDqBak/1hGZcm9vDvNrSTSptTth7iIjI1FL2wNUwDKdhGC8bhvHXch+LVI5YcuRwJgCv20E0oYyriEipHeg/AMC8qnksq1s2oRnXzlCcaCLNrvbQhL2HiIhMLWUPXIGPAZvLfRBSOdJpk3gqjS/LcCafy6mMq4jIBLAHM82rmsfS2qXs6tk1Ie9jmiadoTgAWw72Tsh7iIjI1FPWwNUwjPnAm4CflfM4pLLYPay5Mq4xZVxFRErOXoUzJziHBn8DPfEeTLP0pbz9sSTxlPU5vvmAAlcRkSNFuTOu3wM+DeSMJAzD+KBhGGsNw1jb1tY2aQcm05edUc3e4+okqoyriEjJ7Qvtw+Vw0RRoIuAKkDbTxFKxkr+PnW0F2HKgr+SvLyIiU1PZAlfDMK4AWk3TfHG0x5mmebNpmqeapnlqU1PTJB2dTGd2xjX7VGFlXEVEJsL+/v3MDc7FYTgIuAMAhBKl70HtGAhc6wJuZVxFRI4g5cy4ngNcaRhGM/A74CLDMH5bxuORCmHvac2VcdU6HBGR0muPtNMUsC4wB1xW4BpOhkv+Pp39VuB61tIG9vdE6Q7Hx3iGiIhUgrIFrqZpfs40zfmmaS4G3gk8aprmu8t1PFI5Mj2u2YYzuR2ZwFZEREqnJ9ZDracWIJNxDScmIHAdyLies6wRgC0HVS4sInIkKHePq0jJ2aXAWYczKeMqIjIheuO91HhrgMGMayQZKfn72KXCpy6eAcCeztIHxyIiMvW4yn0AAKZpPg48XubDkAphD1/Ktg7H61LGVURkIvTGejMZ16A7CExUxjWG1+WgqcprvUcsWfL3EBGRqUcZV6k4o2Zc3VbGdSJWNIiIHKliqRjRVDSTcfW7/MAE9biGEjQEPQS91rX3sC5GiogcERS4SsUZfR2OdZu9A1BERMavN2ZN963xDJQKT+BU4c5QjPoqD16XA8OASFyBq4jIkUCBq1Sc0dbh2IFrVCtxRERKpjduBa613oHhTBM5VTgUpz7oxTAMgh4XoZgCVxGRI4ECV6k4o63DsYNZOysrIiLjZweuh2dcJ6LHtSMUpyHoAcDvcRJJqMdVRORIoMBVKs5o63DsYDamjKuISMn0xHqAwYyrz+nDYTgmMONqBa4Bj5OwSoVFRI4IClyl4sQGMq6+HMOZQBlXEZFSOjzjahgGAVeg5BnXaCJFOJ4aEriqVFhE5EihwFUqTnSUjKtPPa4iIiV3eMYVrD7XUu9xtXe4NgzJuKpUWETkyKDAVSrOWOtwQBlXEZFSsjOuVe6qzG0Bd6DkU4U7+63AdWipsDKuIiJHBgWuUnFiyRRup4HTYYy4Tz2uIiKl1xvrpdpdjdMxeMHQ7/KXvMe1IxQDoKFqSMZVPa4iIkcEBa5ScWLJdNZsKwydKqzAVUSkVHriPdR4a4bdFnCXvsfVzq5Wed3We3hchFUqLCJyRFDgKhUnmkhlXYUDQ/e46gq9iEip9MZ6M4OZbAFXoOQZV/uz2zcwwyDgcRJWqbCIyBFBgatUHCvjOnrgqoyriEjp9MR7hg1mAgi6g6WfKjwwn8A/UD2jdTgiIkcOBa5ScWLJdKYk+HA+DWcSESm5rBlX90RkXO2p8dZnud/jIpJIkU6bJX0fERGZehS4SsWJJlJ4xiwVVsZVRKRUeuO9I3tcJ2iPKwyWCgc9VgAbUfuHiEjFU+AqFSeWTGeuxqfNNKn04AmNMq4iIqVlmia9sV5qPcNLhe2pwqZZumxoNJHCMMDjHOxxBVQuLCJyBFDgKhUnNmQ400/X/ZS3/vmtmfuUcRURKa1IMkLSTGadKpw208RSsZK9VzSRwudyYhjWujO/x2UdgwJXEZGKp8BVKk4iNTic6bG9j7GrZxd98T4AXE4HToehjKuISIn0xHoARmRcA64AQEn7XKOJdKZMGAZLhUNxrcQREal0Clyl4iRSJm6ng1AixObOzQDs7dubud/nchBTxlVEpCR6470AWTOuQEn7XKOJ1LDhe36VCouIHDEUuErFiSfTuJ0Gr7S+Qtq0AtShgavX7cysVBARkfHJlXENuoNAiTOuh02NDwyUCoeVcRURqXgKXKXiJFJp3E4HLx56EadhneAMC1yVcRURKRm7FaPaUz3s9kyp8ARmXDWcSUTkyKHAVSpOPJXG43Kw9tBajms4jnpfPS19LZn7fW4nsaQCVxGRUgglQ8BghtU2caXCg6cuduCq4UwiIpVPgatUnEQqjdORZH37ek6ZdQoLqheMyLhGtfNPRKQk7MDUDlRtEzOcyZoqnHmPgVJhDWcSEal8Clyl4sSTaeJGG8l0kpUNK7MGrsq4ioiURihhZVztQNU2GVOFA15lXEVEjhQKXKXiJFImphEBoMZTw4LqBRwMHSSeigPgcTmIK3AVESmJUCKEgYHf5R92u99t/X5Ce1zd6nEVETlSKHCVihNPpTEdUQCqPFUsqF6Aicm+/n0AeF1O7XEVESmRSDJCwB3AMIxht0/MVOHhgavL6cDjcqhUWETkCKDAVSqKaZokUulMxrXKbQWuMDhZ2ONyEE8p4yoiUgqhRIigKzjidp/Th4GRKSUuhcNLhcEa0KRSYRGRyucq9wGIlFIybWKakGIgcPVUUeOtAQYDV69KhUVESiacDI8YzARgGAY+l49oMlqy94omUniHDGcCq1xYpcIiIpVPgatUlMRAJjVlDJQKu6vwu/x4HB4OhQ8BVsZVw5lEREojlAhlDVwBPE5PZr5AKUQTKfyewwJXr4uwSoVFRCqeSoWloiSSJgApM4LDcOB3+TEMg6A7mBkQ4nEq4yoiUirhRHjEDlebx+EhkU6U5H1SaZNEyhy2DgesUmFlXEVEKp8CV6kodu9qkghBdzAzLCTgDmQCV69bGVcRkVIJJ8MjVuHY3A53yQJXe//24T2ufreTcEyBq4hIpVPgKhXFLhVOmGGq3dWZ2wPuQGZAiMfpVMZVRKREwonsPa5Q2lLhwcB1eMY16HURTqhUWESk0ilwlYpiB6QJM0zQM1i6FnQFCSWtwNXrVqmwiEiphBKh3BlXp7t0gevA5/aIjKtKhUVEjggKXKWi2BnXeHp4xjXoDhJJWJOGPU5rHU46bZblGEVEKkk4OXqPazw9sRnXgEqFRUSOCApcpaLEhwSuQ0+khpUKuxzDHisiIsVJm2kiyUjOUmG3w00iVdoe18PX4QQ1VVhE5IigwFUqSiJlZVFjqTBV7qrM7QFXYLBUeCBw1YAmEZHxiSStSpagK0fG1Vm6qcLRRPZSYZ/bmblPREQqlwJXqSh272o0HabKUwWv3Aa3v4ugazDjageu6nMVERkf+3M1Z8a1lD2uAxlX/2Glwh6X1f5hmmr/EBGpZK5yH4BIKdk9rpFkP1XuIDz1P9C+jWBdNZFEBNM0M2VmsaR6okRExsNeM5ZzqvAk9LgOraI5/D4REakcyrhKRYmn0mAkSZoJqmIhaN8GTg+B5mdImkni6fhgj6syriIi42K3YOSaKlzadTh2qXD2wFVzC0REKpsCV6koiWQawxEFINi6BRwueMsPCYQ7AausTSc5IiKlYWdcc00VdjvcJexxtTOuw09dMhlX9bmKiFQ0Ba5SUeKpNDhiAFTvexWWXQyr30bQVwdYJ1keneSIiJREplR4lIxryaYKJ3OVCqv9Q0TkSKDAVSpKIpXGcA5kXMMdsPItYBgEaxYAVsZV63BERErDHs40Wsa1dD2uA6XCh63D8brV/iEiciRQ4CoVJZE0M6XC1ek0zFwJQGDGEgDC0Z7Bq/PKuIqIjEs4OcZwppL2uA7scT2sVNjj1IozEZEjgQJXqSjx1JAe13Qa6q2ANdCwHIBQx7YhGVeVlYmIjMeY63BK3ONqGIM9rTY7kFXgKiJS2RS4SkWJJ4f0uHprwVcLQHAg8xrufE17XEVESiSTcR2txzWdKMmO1Wgihc/lxDCMw97DqqLRZ7qISGVT4CoVZViPa+2izO3BxmMBCHXvGhzOpJMcEZFxCSfCeJ1eXI7sa+E9Dg9ASbKu0UR6xERhGJpxVRWNiEglU+AqFSWRSmPYGde6JZnbA14r8xruaRm2rF5ERIoXToRzDmYCK+MKlKTPNZpIjZgoDKiKRkTkCFG2wNUwDJ9hGM8bhvGqYRgbDcP4UrmORSpHPGXidIRxmyaehmWZ2+0Tq1D/AWVcRURKJJQM4Xf5c95vZ2JLMVk4mkxnDVz1mS4icmTIXtszOWLARaZp9huG4QaeMgzjPtM0nyvjMck0F0+mqXV1402noX5p5na3040bB+FED95EX+axIiJSvHwzrqXY5RpNpEYMZgLtcRUROVKULXA1rUkN/QO/dQ/8N/7pDXJES6TSBJy9+IZMFLYFnF5CDge+8H5AJzkiIuMVToRzDmaCwR7XkmRcVSosInJEK2uPq2EYTsMwXgFagYdM01xTzuOR6S+RSuNx9lOVNodlXAGC7gBhw8AdOgToJEdEZLzCycnNuGYbzqRSYRGRI0NZA1fTNFOmaZ4IzAdONwxj1eGPMQzjg4ZhrDUMY21bW9ukH6NML4lUGqcjQhADAg3D7gu4qwg7HDhCB3E7DQWuIiLjFEqEcu5whVJnXLP3uGYG7iX0mS4iUsmmxFRh0zS7gceAy7Lcd7NpmqeapnlqU1PTpB+bTC+xZBrDiFvDQg7b9Rf01hJyGNB3CI/ToavzIiLjFE6GRx3O5Ha6gdJlXP1ZA9eBPa4pfaaLiFSyck4VbjIMo27g//3AJcCWch2PVIZEyiRlJPFl6bkKuIOEnB7oO4DX7VTGVURknMbqcXU7rMC1NFOFs/e4up3WRcpYQnMLREQqWTmnCs8BfmUYhhMrgL7DNM2/lvF4pAIkkmkSjjT+LKVrQXeQNpcb+g4OZFx1kiMiMh6RZAS/O3fGtZR7XGOJdNapwoZh4HWpikZEpNKVc6rwOuCkcr2/VKZUMkHcMPF7qkbcF3AHCDsc0H8Qj8uhjKuIyDgk00kS6cSopcJ2j2siPf5S4XgqnRnEdDgFriIilW9K9LiKlIo30UnEMPB7a0bcF3QHCRlA30G8Lof6oURExiGSjACMXipcwh7XeDKNx5n9tMXjcipwFRGpcApcpaIE4u1EHQ583rqR97kChMwU9B/C69QEShGR8bAD13wyrqXocY0nx8q4qv1DRKSSlbPHVaTk/AlrR6vfXz/ivqA7SBKTeDpJk7OfeMo32YcnRYokI3zj+W/wSusrLJuxjG+/7tvlPiSRI14+gaudcR1vj2s6bZJMm6MGrmr/EBGpbApcpaL4Uh3Wr4ftcAUyuwbDDgczjS52JUYGtzI1bWjfwF3b76LaXc2h8KFyH46IMLkZV7u1w52zVFg9riIilU6lwlJR/Ga79Wtg5M5fuw8r7DBooouYelynjY6odUHi3Hnn0p/oL8mEUhEZn8nMuNqBa7apwgBet3pcRUQqnQJXqSjedBcAfm/tiPt8Lqs0OGoYNJldKiubRjoiVuB69IyjAeiMdpbzcEQEiCTyz7gm08lxvZf9eZ2zVNjpIK4eVxGRiqbAVSqKn27r1ywnUl6nF4CYYVBvdmqQxzTSEenAaThZUrsEUOAqMhXkVSpcoj2uduCaq1TY61apsIhIpVPgKhXFa/YCg9nVoXzOgYyrv476dIcyrtNIR7SDGb4ZNPobAeiKdpX5iEQknAwDY5QKOwZKhcfZ45oYKBXOtQ5Hw5lERCqfAlepKB6jH8geuHpdVsY16q+nLtWpq/PTSEekgwZfAzN8MwBlXEWmgnwyroZh4Ha4S5ZxzVUqrOFMIiKVT4GrVBS3EQKyn0jZwWzMX0NNqltX56eRjkgHjf5G6n3WJGgFriLllwlc3bkDV7Cyrol0YlzvFRurx9XlVPuHiEiFU+AqlSMeAoc1AMTvzBK42qXC3iqCyW6d5EwjHdEOGvwNVLmrcDlcKhUWmQLyybiC1ec63oyrSoVFRESBq1QMs7+ViGEA2TMA9nCmqCdIMKmpwtOFaZq0R9pp8DVgGAb13nplXEWmgEgygsvhyvSx5uJxeMadcVWpsIiIKHCVipHqPUh0IHC1s6tDZUqFPX68qRBuM05Su1ynvL5EH4l0ggZ/AwD1/nplXEWmgEgyMma2FaxdrqXa45q7VNhBLKHPcxGRSqbAVSpGqvcgYcdA4DraVGG39Ws9fbpCPw3YO1ztwHWGd4YyriJTQN6Bawl6XDMZ15ylws5McCsiIpVJgatUjFR/B1HDwIkLl8M14n57qnBs4Nd6o1flwtNAJnD1DWZcFbiKlF8kGSHgCoz5uFL2uOba4+pxOUilTVXRiIhUMAWuUjHMcAcRw4HbMTLbCtZVf6fhJOq0gtpGo1cZ12mgI6qMq8hUlG/G1ePwjHuP69hThR3DHiciIpVHgatUDDPcQb/hwu3w5nyM1+klOpCNbaBHGddpoD3SDgxmXBv8DYSTYaLJaDkPS+SIl3fg6vSQSJWmVNg7RuCqz/Sp7w/b/sD7739/uQ9DRKYhBa5SOcJd9DrceHJkXMHqfY05rG/7eqOPeEorcaa6jkgHDsNBnbcOsDKugAY0iZRZNBmd9OFMuUuFnYAyrlNdIpXgx6/8mLWH1tIf7y/34YjINKPAVSqGEemgz3DhyTJR2OZz+oiSJu1w02j0EtUUyimvM9pJva8ep8M6Ma331Vu3x1QuLFJOkzmcKZF3qbAuRk5lD+5+kNZIKwAHQgfKfDQiMt0ocJWKYUS6CBlOvKMErl6Xl1gqRtzXQD29mkI5DXREOjJlwgAzfFbGtTOiwFWknCazx3WsdTgelQpPC7duvjWzU12Bq4gUSoGrVAxntJOww4F3tFJhp49oMkrS10CD0au9f9OAnXG12UFsV0ylwiLlVI4e19zrcDScaarb17+P9e3rue7Y6wA40K/AVUQKo8BVKoYz2kXUcOAb5UTK6/QSTUVJ+RtoMHqUcZ0GeuO91HhrMr9XxlVkaigkcB1/j6sJgNtpZL3f61aP61R3MHQQgDPnnInL4VLGVUQKpsBVKkMqiTPeS8ywsqq5+Fw+YskYpr+BBvqIJdQPNdWFE2GC7mDm90F3EAODvkRfGY9K5MiWNtNW4OqenB7XeDKNx+nAMLIHrnYmVj2uE8M0zXFPcm8LtwEwKzCL2YHZ7A/tL8WhicgRRIGrVIZoNwYmcYc5agbA5/QRS8Uwg000GOpxnQ76E/0EXIHM7w3DoMpdRSgRKuNRiRzZ7CAm3+FM4+5xTaZz9rcCeN0qFZ5IX3r2S5x262lcdMdFPLL7kaJeozVsDWVqCjQxp2pOJgMrIpIvBa5SGcJW2WjCSONzjT6cKZKMQLCRgBEjFdU4/qksbaYJJ8NUeaqG3R70BBW4ipRRJBkB8gtcS1MqnBo9cNVwpgnzwsEXuGv7XVyw4AJqvbV8+bkv0xvvLfh12iJteJ1eajw1zAnOYX+/Mq4iUhgFrlIZwh0ApBwpAqOUrnmd1lRhI9gIgDHwPJma7JPjoCs47HZlXEXKq9DAdbzDmRJJM2d/K2g400RJpBN89bmvMq9qHt88/5t87dyv0RXt4oev/LDg12oNt9Lkb8IwDOYE59AWaRt3CbmIHFkUuEpliHSSADBMAnmUCjurZwHgjLRPzvFJUewF9UHP8MA16A5qeb1IGRUSuLodbpJmkrRZfFAZT41RKuwaGM6kuQUltebAGnb27OQTp3wCv8vPioYVvH352/ndlt9lSn/z1RZpY2ZgJgBzq+aSNtMFv4aIHNkUuEplCHcScVhX44OeQM6H+VzWOhxnlZVxdUSUcZ3KQkkrq3p4xjXoVqmwSDkVmnEFxpVds4cz5ZIpFdbcgpJ6dv+zuB1uzpt/Xua261deT8pM8fDuhwt6rdZwK02BJgBmB2cDqFxYRAqiwFUqQ7iDqGF9Owfco/S4DqzDcVVZu0Bd2gU6pYUTYYBhU4Xt3/cnlHEVKZdCM67AuPpcY8k07lECVzsbq93cpfXcgec4eebJw/6el9YuZVndMh7a/VDer2OaZqZUGGBucC6ABjSJSEEUuEpliHTSP3ByVOUePeOaNtMQqAXAHeuejKOTItnB6eGBa5W7SoGrSBnZgevQid+52BnX8QSuiVQ6k1XNJlMqrB7XkmmPtLOtaxtnzj1zxH2XLLqEFw+9SHue7TahRIhIMpIpFVbGVUSKocBVKkO4g153DTB6qbDX6QUg5vKSMg3c8e7JODopkl0OnC3jamdjRWTyFVQq7ChRqfAogatHU4VL7rkDzwFw1pyzRtx3yaJLMDF5dM+jeb1Wa2RwFQ5YF5GrPdV0RNWuIyL5U+AqlSHcRbfbWplSPUrgap9kxcwEPUYVXgWuU5oduFa5h6/DqfJYU4XzHfayvm09F91xEXt795b8GEWORAWVCjutapjxTBYeaziT02HgchjEkhrOVCrP7X+OWm8tx9YfO+K+ZXXLWFC9gKf2PZXXa7WF2wCY6Z+ZuU3T4UWkUApcpTJEOukdGOATHGMdDkA0GaWXaryJ7sk4OimSfVITOKz8O+gKYmJmTp7H8vT+p2mLtHHblttKfowiR6JielwTZvGBayI1eo8rWFlXlQqXzob2DZzUdBJOh3PEfYZhcMyMY9jVsyuv17KnB9ulwqAheyJSOAWuUhnCHfQ4rROow4OcobyuIYGrUYMv0TMphyfFyVkqPLAeJ9+VOJs7NgPwp9f+pBJjkRLIBK6jXCi0uRwuYJwZ1zGmCoMVuKpUuDTSZpq9fXtZXLs452MW1y6mpa8lrxLwtoiVcbVLhUGzCkSkcApcpTKEO+l1WtOE7eA0G9/AY2KpGH2OGvxJBa5TWSgRwmW4Mplym106nO/V+i2dW1hcs5j+RD9/3fnXkh+nyJEmkozgMByZ/tXR2BnXpJks+v3G6nEF8DgdJLQOpyQOhg4ST8dZWLMw52OW1C4haSZp6WsZ8/Xawm0E3cFhFyE1q0BECqXAVaa/dBoinfQZVlDqd+ZRKpyK0u+sIZjqnZRDlOKEEiEC7gCGYQy73T75ySdw7Y52sz+0n7ce/VaOrT+We3feOyHHKnIkiSaj+Jy+Ef82sylFxjWWT+CqjGvJ7O7dDcCi6kU5H7O4ZjEAzT3NY77e0FU4Nq01E5FCKXCV6S/WA2aavoGr+qNlXDPDmZIxws4agmkFrlNZKBEaUSYMgxnXfE56NndaZcIr6lewqnFV3j1ZIpJbLBXD58q9M3uoTMY1XXzGNZHKr1Q4poxrSezp3QMwasbVLiPe1Tv2Z2pHtIN6X/2w24LuIKG4elxFJH8KXGX6C3cC0GdYV/XtcuBs7IxrJBUh7KzFY8YhrlKlqSpX4FpIxnVo4LqoehFdsS56YioRFxmPSDIy6mftUJmM63jW4YwxVRisUmFlXEujubcZn9M3bJjS4Wo8NTT4GvLKuHZHu7MHrkkFriKSPwWuMv0NBK79WCdHh/dDDmVnY2PJGBFXrXVjpHNij0+KNlbgmk/GdUvHFuYG51Lnq2NRjVX2ZmcTRKQ4sVRs1OqWoUqRcc1nOJNXpcIls6dvDwtrFuIwRv8zX1y7mObe5jFfryvWRZ2vbththa41ExFR4CrT30DgGTKcYLqyju63DR3OFHUPBK5hBa5TVSgRGrHDFQobzrS5czMrGlYAZALX3X27S3iUIkceu8c1HyXJuKrHdVLt6d2T+bwczZLaJWO2X5imSU+shxneGcNuDw6ssNOAJhHJlwJXmf7CHdYvGBi4R32o3ZMVTUWJe+qsG5VxnbLs4UyHy7dU2DRN9vXvy/Rpza+ej8NwZAaPiEhxosloXjtcYfwZ13TaJJk289rjGleP67gl09ak4IXVuftbbYtrFtMd66Yr2pXzMX2JPlJmijpv3bDb7bVm2uUqIvlS4CrT30DGNAI4zNFXM9gZgmgyStyjjOtUl6tU2O1043V6xywV7o33kkgnMtMsPU4Pc4JzFLiKjFM0FR21LWOo8WZc7WB0rIyrWz2uJXGg/wBJM5l3xhUYtVy4O9oNwAzf8IxroWvNREQUuMr0F+4Aw0nUTOMYI+M6dB1OQhnXKS9XqTDkN5GyPdIOQKO/MXPboppFClxFximajE7aVGE7cPVqj+uksFspRpsobFtQvQBg1F2uXTErG1vrrR12eyFD9kREQIGrVIJIJwTqSRLHaYyecXU6nLgcLmLJGEm73yacu8RJysc0TcLJcNZSYchvB2BbpA0YGbju6d2DaZqlO1iRI0w0lX/gOt6Ma2Igi5pXqbAyruN2IHQAgHlV88Z87NyquQC09OcOXDMZ18N7XAsYsiciAgpcpRKEO8FfT9qM42D0wBXA7/QTS8Vwun2ETJ8yrlNUJBkhbaazlgqDVWY21pX6tnD2wLU/0U9nVH/vIsUqZjjTeDOu+QxniilwHTe7UqXB3zDmY71OLzP9M9nXty/nY+yM64ipwioVFpECKXCV6S/cCYEGUsRxGWP3XHldXiLJCF63gy6q1OM6RdknM6OWCo9xwtMRsQZ32T2uMGSysMqFRYpWSMbVLhUuusd1IBjNax2OSoXHrSPSwQzvjMzf21jmVc9jX3/uwNXem314xtWuplHgKiL5KlvgahjGAsMwHjMMY5NhGBsNw/hYuY5FprmBUuF0HqXCYF0hjqVieJwOuswqTAWuU5J9MpOrVDivjGukDb/LPyxrm+nJGqW0TURGV0jGddw9rsk8M64azlQS7ZH2vLKttnlVoweuXdEuXA7XiOoZ+6KkSoVFJF/lzLgmgU+aprkSOBP4qGEYK8t4PDJdhTsygasrj8DV5/QRS8WsjKtZjTmwTkemljEzrp78elwbfA0YhpG5zc6+2uVwIlKYtJkmlopNXsY1pR7XyVRM4HoofCjn3293rJsZ3hnDPodBw5lEpHBlC1xN0zxgmuZLA//fB2wGxp4EIDKUaWZ6XE0jgduRX6lwNBnF43TQTRWmelynJPtkJlePa9CVX6lwU6Bp2G0Bd4CAK6DAVaRIsVQMoODhTOPNuI45VVilwiXRHmkfNhdgLPOq5pE20xzsP5j1/q5o14j+VrDWk7kdbgWuIpK3KdHjahjGYuAkYE2ZD0Wmm3g/pBMQqMckgTuPHtfBjKuTTrMaI6KpwlPRWKXCQU+Q/vjYGddsJ2CN/kYFriJFiiajAHnvcTUMA5fhGn+Pax57XFNpk1RaE8OLZZomndFOGn35B67zq+cDudsvumPd1Hnrst6XT8uHiIit7IGrYRhVwF3Av5im2Zvl/g8ahrHWMIy1bW1tk3+AMrXZZb6BBkwjjtuRR6mwy0c0GcU7kHE1oj2QTk3wgUqh7DLgXKXCVe4q4uk4iVTuk+H2cPbMQaO/MTO4SUQKY2dc/S5/3s9xOVyTMlUYULnwOISTYSLJSEEZ1/lVoweuXbGunIFrwB1Qj6uI5K2sgathGG6soPVW0zT/mO0xpmnebJrmqaZpntrU1JTtIXIkswcr+evBSODOIwPgdXqJpqJ4XFaPq4EJke6JPU4pWDgRBkYpFR5jB2A0GaUv0TdsorCtwd+gjKtIkSLJCEDew5nA6nMteo9rvj2uA/erXLh4hazCsc0MzMTlcOVcidMT6xkxUdimjKuIFKKcU4UN4BZgs2ma3y3Xccg0NxC4pvx1GI4k3jx6XDOlwi5rqjCgXa5TUCg5eo9rtacaIGe5sH0CplJhkdLKlAq78isVhnFmXAtYhzP08VK40T43c3E6nMwJzsk6WThtpq1S4Sw9rpDfWjMREVs5M67nAO8BLjIM45WB/95YxuOR6Wgg4AwPlJN68glcB0qFPS6rVNh6AQWuU01/vB+H4ciZ1al2W4Frb2JEhwEwduDaG+8lnoqX6GhFjhyZUmFn/qXC48m4xvJdh+NSxnW8ism4Qu6VOH3xPtJmOmfGNegee1aBiIjNVa43Nk3zKcAY84EioxkIOPsHplt68phyaZcKe11OuszqgddRv+NUE06GCbqCI1Yo2Ko8AzsAi8y4gjV1eE7VnFIcrsgRI1MqnOdUYRhfxjWRsoYtjZVxVY/r+BWTcQUrcH1s72Mjbu+KWsMPc2Vcq9xV7OnbU9hBisgRq+zDmUTGJdwBGPRjZVrz6bnyuXzEkjGrxxWVCk9V/fF+gp7sZcIwdqlwW8Qa5nb4OhwYPClTubBI4YopFXY7i8+45jtV2ON0Dnu8FK4j0oHTcOYcppTL/Or5dEY7M7MJbN2xboCcr5fPdHgREZsCV5neIp3gr6MvaZWu5XMilRnO5DToNlUqPFXZGddc7MC1N567VNjAyFqiZpfB2cGtiOSvmFJhlzGeHldr6rumCk+8jmgHDb4GHEZhp4fzquYBjCgX7oha1UwzfDlKhfPYxy0iYlPgKtNbuAMCDYQSVumaP4+pwnZ5m+FI0o+ftOFSxnUKCiVCOQczweCanFxThbuj1u5Ap8M54j57R6EyriKFs0uFJy3jWug6nJTWmxWrPdJecH8r5A5c9/fvt+4Pzsv6vKAnSDQVLfqihogcWRS4yvQW7gR/PaG4VbqWT8+VXU5sGnHAIO6pVcZ1CupP9OcVuPbF+7Le3xXrynmVv95fD6BdriJFiKYGPm8LWIfjMlzjWIdj9bi6naOPxbDvjynjWrT2SPbd12PJFbi29LVQ5a6i1lub9Xn257iyriKSDwWuMr1FOiFQn+mr8bnGLl3LZAkM6yQq5q5TxnUKCifCowauToeToDuYO3CN5l5673a4meGdoYyrSBFiA60Z/jw+b21up7vorFqswHU4dqArhSs2cK331eN3+Wnpaxl2e0t/C/Or5+ccsmd/xitwFZF8KHCV6S3cOVAqbGUAAgVlXK3ANequhXDXxB2jFGWsjCtYV+tzlQp3Rbuo99XnfG6Dv0GBq0gRIqmBUuE8WjNsLoeLRKr44UwepyNn8GPTcKbxMU2TzmjnqJ+buRiGwbyqebT0Dw9c9/btZX7V/JzPC7gCACOGOomIZKPAVaa3cCf4ZxAZmHIZcOe3DgfAxDqJirhqlXGdgjI9rvtfhgc/D3e8F/a9NOwx1Z7qUUuFc61gAGuycHtUgatIoaLJKG6HO2v/eC5uh5ukWexwpvSY/a2g4UzjFUqESKaTOVssxjK/av6wUuG0mWZf3z7mV+cOXO2svd03LSIyGgWuMn3Fw5CMQKCBsJ1xdY9dumb3waaJAxBxqsd1qjFN0yoVTqfgl2+GNTfDzsfgF2+ETfdkHlftqR5cpWCa8OIvYc3NpM00PbGenEvvwQpc1eMqUrhYKlbQDlcYX8Y1kUqP2d8KGs40Xl0xq/Ko2MB1XvU89vXtwzStUu22cBvxdHz0jKvbyrgqcBWRfLjKfQAiRbOzpIF6IhHrh14hpcIp0wpcQ84a67VME8YoRZPJEU1FSZkpgq89CmYKProGPEH43bvgjhvg9V+Acz9BlbvKWrfQsQPu+wy89hAAfR4/KTM16glYg88qFTZNc8wSRBEZFE1GCxrMBMq4Tgfd0W4g987Vscyrmkc4GaY71s0M34xM2bAyriJSKsq4yvQVHsiW+euJDAwLCXryH84UT8dwOw36nTWQikNcwyGmCntQR7B1K7z+i1C/BKpmwnv/CqveBo98Gf70EaoMF31du+AHp0HzU3D5t2DhWXQ9+Flg9MxBg7+BWCpGOKneKpFCRFPRSc24xlN5Bq5OBa7jYWdcxxO4wuBkYXtQUz6Bqz6HRSQfClxl+rLLewMNxArocbUzBbFUDI/TQZ+jxrpDfa5Thj2oI+itg9M/MHiH2wfX/Awu+By8ehs1m/5iDWc6/QPwsVfhjA/C239Jl9MqJhmtVNgeQNKpv3eRgkST0YIGM8FAxrXIqcLxVBr3GBOFYTDjqnU4xemOdQOjf26Oxg5c7UxrS38LBgZzg3NzPkcZVxEphAJXmb6Glgono5imQdCT/3CmaCqK1+2k1xgIXNXnOmX0d+8BILjoXDh8AIxhwAWfhWtuoapuMX0uD+ZlX4fqWdb91bPpWnIuADOM3MNj7MC1I6o+V5FCRJPRglbhwEDGtcg9rvZU4bFkMq4pBa7F6IoOZFxHGWo3mgXVC3AaTrZ2bgWsjOvs4GzcTnfO5yhwFZFCKHCV6WtoxjUVBdOFxzX2lEu7xC2ajOJxOuil2rpDmbcpI7TtXgCCyy7N/aDVb6PqxOtJmimiqeiwu7oWngbAjB1/z/n0er8CV5FiFFMqPK6MazKd2dE6GvW4jk93rBuX4aLKXVXU8wPuACc0ncDT+54GrMB1tDJh+zmgwFVE8qPAVaYvO3D1zyCeimGm3Xn1QQ0tFfa6HXRTNfz1pOxCOx8BoKrp2FEfV+OxsuWZycIDunzW32ndq3dYQ7eyaPA1ANAZ1d+7SCGKKRUed8Y1j892p8PA6TBIKONalK6otUJsPMPqzpt/Hps7N9Pc08z27u0srlk86uM9Dg8Ow6E9riKSFwWuMn2FO8BbC0430VQMTHdeKxPs4Ux2xrXLDlwjXRN5tJKvrt2EevYCg1fjc7EzA32J4btcu6Jd+A0X/vZt0L4963PV4ypSnGJKhceTcU3k2eMKVrmwMq7F6Y51Fz2YyXbuPKtN49NPfppQIsQ1R18z6uMNw8Dv8ivjKiJ5UeAq01ekEwLWEIlEOgZpd159UHamIJaK4XE56DarAANCbRN5tJKvHY8Sclh/j0F3cNSHVnkGAtf48MDVWsdgBab2ipzDeZweqt3VyriKFCiaKnwdzrgyrnlOFQarXFiBa3G6ol1F73C1HTPjGJr8TWzu3MyZc87kuMbjxnyOAlcRyZcCV5m+wp0w0KcYT8UwTXdeV+UdhgOv02uVu7kcRFIGBJug7+BEH7HkY8cjhPx1AGP2WuUqFe6MdlIXaITG5fDawzmfX++vV4+rSIGsz87CS4WT6SRmjtL90eQ7nAkGAleVChelFBlXwzAyWdebVt+U13P8Lr/W4YhIXlzlPgCRooU7rIATiKejkM4vcAUr6xpNRfG4HNbqhKpZ0N86kUcr+UglYeeThJYejxHdM2Y5YqZU+PCMa7TbyhwsuwRe+BnEw+AZWXbc4GvIZFxN0xxXb5fIkaKYjKvb4cbEJGWmcBmFnXrk2+MKVqmw1uEUpzvWXfQqnKFuXHUjR9UdxRmzz8jr8cq4iki+lHGV6SvSCQEr45ow4wMZ1/wCD5/TZw1ncjmtk5zqWdCvjGvZ7XsRYj2EauYSdAfHDCQzpcKH97jGuqwTsKMvhlQMmrNPF6731dMW7uBzf1zHaf/1CBv29ZTm6xCpYLFkrKh1OEBRfa7xVP4ZV69KhYuSNtNWxrXIVThDLa5dzHuPe2/eFwIDroACVxHJiwJXmb7CnRCwJsMm0zEM0533D0qvyyoVzvRDVc2GvkMTebSSj9ceAsNBKDhjzMFMMMpUYbtXa+HZ4A7kLBeuctexu/sQv39hL8l0mg/+ei1tfbHxfx0iFSqRTpA0kwVPFXY73JnnF6qgjKsC16L0xftIm+mSZFwLpYyriORLgatMT8k4xPszPa5JM44DT95PH9rjGkumrIxrqBXSOuEpG9OEDXfBonMIpZNjDmYC64THaTiHlQrHUjHCybB1Aub2weLzYHv2AU0b96ZJG2F+fdOp/PamM+gMx/n8n9aX7EsSqTTRpLUzudA9ruPOuOYZuLqd6nEtRlfUmqpfioxrofwuv9bhiEheFLjK9BQeGKgzMFU4mY4VFLj6Xf7MVOFYYiDjmk4Ovq5Mvn0vQudOOOGdhBKhMQczgTUIJOgODgtc7fU29robjr4EunZBx45hz31mRzvr96QwDJNj5zlZNa+WD5y3lAc3HWJvp06iRLKJpayKhMnMuCaSBazDcTm0x7UIXTErcC1LxtWtjKuI5EeBq0xPoYFBSlWzAEgSx4E776fbw5n87iE9rqA+13Ja93tw+WDFlYQSobxKhQGqPdX0xnszvz8Utkq+mwLW4C6WXWz9OqRcOBJP8bk/rqdxIGPfEbEuWLzrjIU4DINb1+wZ71cjUpHGG7hOdMZVe1yLU+6MqwJXEcmHAleZnvqHB64pM46T/E+kvC6vNWDE7SSaSFkZV1Cfa7mkElaZ8DFvBF8NoWSIoGvsUmGARn9jJvAEaItY+3hnBQYuRtQvgYZlw8qF/+fhbezuCPOhc08EyEwWnlPr5+IVM7lj7V7r+0JEhoklrcC12FLhQjOu6bRJImUWtg5HgWvBumPdQJkyrgpcRSRPClxleuofCDCrZmKaJiliOI38A1ef02dlXD1OIokUZtXMgddVxrUsNvzRKtM+8V0AhOKhzMTgscwMzMxkWQFaw62Z2zOWXWxNFk5E2NnWz8/+vpPrTl/IeUctBgYDV4B3n7mIzlCcJ7e1jfOLEqk8dsbV48y/NQOKz7ja/aqFDGfSOpzCZTKu49zjWoyAK0A0GS1qx6+IHFkUuMr0ZAeuwZnE03HAxGXkfyLlc/mIJqP43E5SaZOE3w5clXGddOk0PPU/MHMlHPV6AELJEAFXfqXCMwMzM8EqWKXCbod7+AnY8jdAMgrbH+L25/fgMAw+fsnRNPisqdRDM7anLa7H5TB4eW/3uL80kUpjB67F7HGFwjOudr9qQRlX9bgWrDvWjdfpLXjNUSn4XX5MTKKp6KS/t4hMLzl/EhiG8X+GYZwzmQcjkrf+VvDWgCdAJGGVGLkc+Z9I2Xtc/W4nABE84K1VqXA5bH8Q2jbDOf8CDgemaRKKh/KaKgxW4BpOhgklQgC0hduYGZg5fDXS4vOhahapV3/HnS+2cMnKWcys9lHjqcFluIZlXH1uJyvm1PCqAleREYrNuBY7Vdgu+8034+pVj2tROiId1Pvq814pV0p2sKzJwiIyltF+EmwDvm0YRrNhGN80DOOkyTookTH1H4KB8l77Kq2rgFLhzHAmjxW4RhMDK3FUKjy5OnbAfZ+G2oWw6moAIskISTNJjbcmr5ewS4LtcuHWcCtN/qbhD3K6YPXbMbY9iBnu5LrTFwLWVOJ6X/2wwBXghAW1rG/pIZ1W6ZrIUOPNuE5GqbAC18J1RDsyFSiTzQ5c1ecqImPJ+ZPANM3vm6Z5FvA6oAP4uWEYWwzD+KJhGMsn7QhFsulvzQxmCietq7RuI/8TqaHDmcCaMkvVLGVcJ0M6Dc/9GP7wfvjZ6619vG//BTitE1t7QnCNJ7/A1R7CZJcLt4Zbh/e32m+7+h04zATvqX6Rc5c1Zm5v8DeMDFzn19EXS7KzPVT41ydSwcabcS20VDiTcc2zVFh7XIvTEemgwV+mwNWtwFVE8jPmTwLTNHebpvkN0zRPAq4DrgI2T/SBiYxqaMY1aWVcPY78M65+p594Oo7HOpcikkhB9WxlXCdaKgH3fBTu/wzsfwnmnQo3PQTzT808xA5cqz3Veb2kHaSOFbj+YV8dm9MLuDHwNI4h1XD1vvphPa4AJy6oA1C5sMhh7M/bQqcKu53j63F1K+M6oTqiHTT6G8d+4ASw5xkocBWRsYz5k8AwDJdhGG82DONW4D5gK3D1hB+ZyGiGZFztH3aeAkrXvC4ryHW5rJUnkcSQjKsmG06MeBh+/2549Ta48N/hn1+Bd98JDUcNe1hfvA8YO+P6t/UHePuPn8FDHWAFrP3xfsLJ8IjAtTsc5+v3beGpuiuZ0bMRWtZm7stWKry0qYoqr4tXW7qL+1pFKlQ8FQcK3+PqMorrcY0VmHH1uByZYFfykzbTdEW7qPfVl+X9VSosIvkabTjTJYZh/BxoAT4A3AscZZrmO03TvGeyDlBkhHgYYr3jyrjaJ11Op3USFY2noHoOJCMQ7S7t8YqVaf3tNbDtAXjTd+F1n4YcQ0B6YwOlwqP0uLZ0hfnMnet4obmL7z+0m2p3Na3h1uyrcIBvPrCV3miS89/+T9ZQrzU/ztxnB65DVzE4HQar59XyyhgZ12Qqzbcf2MrvX9hDT7iwTJLIdGTPFCg0cC0242pnT735ZlydDhIpU/3pBeiOdZMyU2UrFbYzrhrOJCJjGe0nweeAZ4AVpmleaZrmbaZpquFLyi80sPrksIyr15n/GH97sIjTYQWukUQKZiyy7uxqLs1xyqDtD8GeZ+DN34PTbhr1oZkeV3f2wDWdNvnXP6wjbZpcdeJcfvfCXqrdDVbgGhkZuL66t5vbn9/D+85ezDEL5sBJ74ZNf4LeA4DV4xpNRUdc7T9xYR2b9vda/c85/PKZZn7w2Gt85q71vOF7T1pDvkQq2HgzroWXClsBqNuR37Rbe4iT+lzz1x5pByhfj6syriKSp9GGM11kmubPTNPsmswDEhlTf47AtYB1OHapsOGwTsKswHWJdWfnrhIdqGS8cisEm+DE68d8aKZUOEfG9ZfPNPPszg6+8OaVfO3q1cyr89Pe4+Ng6NCwjOvezjBf+esmPvzbF2ms8vIvFx9tvcDpHwDDAXe8B2J9mfK4w/tcT100g2TazFkufKg3yvce3s7rljfxvXecyMHeKE+/1p7Pn4bItGVnXAsdzmRnXAsqFU7GOOZv17LOexOn/fkCSMbGfIpXgWvB7M8+TRUWkakuv9obkamkf2Dy70CpsP3DrpDF6X47O2tnXOMpmLHYuq1LgWtJhdph2/1w/Dsyk4NHY2dcq9xVI+57rbWfb9y/hdcfO5NrT11AwOPiS1ceRygUpLn7QCZwNVK1XPfT5/jNc7tZUB/gf995EtW+gfeuXwpv+znsewluv476gffpiA4PXE9ZNAOAF3dnv3b3vYe3EU+l+dKVx/HG1XOo9rp4cKOmUktli6fiuAxXZkpwvjJThVMFZFz3v0xt2wtsN+fj7W+BPc+N+ZRMxlUDmvJmf/Yp4yoiU50CV5l+MoGrlXG1e1ztLGo+MhlXwzqJiiZS4K2C4ExlXEtt/R8gnYQT35XXw/vifVS5q3A6nCPu+/YDW/G5nfz3NasxBnpkL145i8V1c+hPdvGLNS/jMANc/9OX6OiP84d/OIvf/8NZnHXUYSdkK94Mb/0xND9F/d+/BzBiQFNdwMOymVWsbR5+O1iTTv+2/iBXrJ7D4sYgHpeDC46dycObD5FSb51UsGgyWtBnrS2zx9UsIOO6dw0A/5L4CKbhgh2PjPkUe4iTAtf8lTvjGnBrqrCI5EeBq0w//a2AAQFrdL/9w84e8JCPTH/WQOAasXsT65eox7XUXrkV5pwIs47L6+G98d6sE4V7Igke3dLK1SfPY2b18LLwa05YiWGkCblewm02Mqvax803nMIJA2ttsjr+Wrjif2jY9TQwMnAFq1z4xd1dIwa9vLCrk55IgkuPm5257ZKVs+gIxXl5j7orpHLFU/GC+1uhyIzr3ufpDy5krzmLyJzT4LU8AteBjGtMgWveOqIduB3uvHdnl5rb4cZpOBW4isiYFLjK9NN/CIKN4LROhKKpKKbpwO/Ov+fKLk1KMdDjGh84yZmxRBnXUjqwDg6utwYi5ak31pt1h+sDGw4ST6V5y4nzRtx3VL11W0MgwK+v/BZ3fvhszju6aew3O/X91B9zBQAd/ftH3H3Kohn0RpO81tY//Fg2HsTndvC65YPvccExTbidBg9tVrmwVK5oKlpU4JrJuObb42qasPd5OmacCEB80YVwaAP0jb5r2+uyKjWUcc1fR6SDel99poplshmGgd/lJ5zUVGERGZ0CV5l+eg9A1WCmK5wIQ9qTudKeD3uqcCwVxeNyEE4MnEzVL4HefXkNAZE8vHIbOD2w6pq8n9Ib7806mOmeV/exuCHACfNrR9x32uzTeP+q93Prm25lZcPKgg7Rc/bHqE6l6dw7sn/utMXW4KY1uwazsaZp8uCmQ5x3dBN+z2A5c43Pzap5tby8p7ug9xeZTsadcc13qnBXM4RaOVR7AgDJpa+3bt/x6KhP82Yyrprwna+OaEfZ+lttfpdfGVcRGZMCV5l+unZB/eLMb0OJCGbanbnSng+/e3AYhN/ttPa4wsBkYRO6dpfwgI9QyTisvwOOuRwC+S+27433Uu0ennFt7YvyzI4OrjxxXtasQLWnmk+c8glmB2ePuG9M80+h3uGms3U9pIZngxY1BFg2s4o71+7N3PbSnm4O9ER5w3Ej3+v4ebVs2NejPlepWJOWcd37PAAHqlcD4Jiz2moPaX5q1Kf53NbPAZUK568z0lm2/lab3+UnklDgKiKjU+Aq00s6ZV2Jr1+auSkUj4BZWMZ16BRDv9s5vMcVNFm4FDb+EcIdcGL+ZcKQPeP69GvtmCZcunJWKY8wo6F6Hp2paGYYjM0wDG44axGvtvTwyt5uAH7w6HbqAm7ecNzIYzl+fh3heIqdh5UWi1SKYjOuTsMKKPPOuLY8D55q2vzWZ73b5YBZK6F926hP87oHMq4JBa756oiUP+MacAeUcRWRMSlwlemlpwVS8WGBazgRHsi4Fl4qHElG8HucRBJDelxBfa7jlYzBo/8Fc06AZRcX9NS+eN+IISHP7+qk2udixZyJGR5SX7eEDqcTtj8w4r6rT55PldfFr55p5qU9XTy2tY0Pnr90cL3OECcssMqYX23pmZDjFCm3YqcKG4aB2+HOP+Pavh2ajiGWtiosPC4HNC63Alczd0WDSoULkzbTdEanSMZVgauIjEGB63TQsQOe/l/44z/AvZ8cs8enonXutH4dGrgmo4X3uLqswDWaiuJzO609rmANffJUKeM6GtO0elcf+Hd49ocDU54P88It0LMHLv5PcOT/95JIJ4gkIyOGM63Z2cnpi+txOiZmeEh9YBadHi9sf2jEfVVeF9ecPI+7X97HO3/yHA1BD+89a3HW11nSWEXQ42RdS/eEHKdIucVTcTzO/AfhDeVyuPLPuPbug9r5mZJfj3MgcI32QKgt59PslhGVCuenN9ZL0kyWPeOq4Uwiko/CNojL5OvYAT86B5IRqJkHsT5Y+3N48//Cye8p99FNvkzgelTmpkgyjGl6Csq4OgxHpqfG73ZYe1wBDAOajoGDG0p51JUjnYL7PwvP32wNXUrF4ZEvw5kfgvM+Ze3C3f0MPPoVWHoBHHVRQS/fF+8DGJZxbe2LsrM9xDtOW1DKr2SYen893aRJtm7C1b0X6oa/12cuP5YF9QG2Huzj4pWzCHqzf3Q6HQar5tWyThlXqVDRVJRZzuJK9l0OV34ZV9O0qmuWX0YilcbtNKze9sajrfvbt0HVzKxPVca1MIfC1hT0pkAeU9gnkN/lpzWc5SKoiMgQClynMtOEv34cnG748NPQcBTEQ/D798Cf/xEiXXDOP5f7KCdX505w+aB6TuamaDJacKkwDJYmWaXCQ05yFpwBa38BqYT1Zy+WeBj++AHY8lc4+5/g4i9D5w548lvw1P/Aujtg3inWrsWaefDWm3O+1PMHnmdhzcIRw5R6Y70AwzKuzw9M9D1j6cRlBBp8DZhAt8NB4/YH4LT/N+z+gMfF/ztvafYnH+aEBXX88plm4sl0QVUAUtnWHFjD3Kq5LKieuAswk2E8GVe3w51fxjXcCcko1M4n3p62sq1gZVzBClwXn5v1qXaPa1Q9rnnZ178PgPlV88t6HCoVFpF86KxqKnvhZ7DrCbj4i1bQCuAJwnW/g+Ouhof+Ax764qj9PhWnc6fVhzqk/DSaikDaU9BUYbD6XDPDmeJDA9fTrQz3gXWlOurpLxmH314NW+6Fy78Jl37V+jtoPBquvhlufNAq327fZv35vfcvUJ09K3P39ru56cGb+MpzXxlxX2/cClxrvYMrb57f1UnA4+S4uRPT3wpQ77OmHnfMWGAF3uOwel4t8WSabYf6SnFoUgFa+lr40MMf4r/W/Fe5D2XcoqloZkZAofLOuPYMTPGunT/8AlD1XHAHrf7XHDKlwgllXPPR0tcCwLyqkfuxJ5MCVxHJR1kzroZh/By4Amg1TXNVOY9lSjFN+Pt3rHLLoy6CU24cfr/LA9f8DPx18PT3INIJb/ofcA78dSbj0Px32POslZWtmQcn32D1b053nTuHlQkDxFIxTNNdcHbL7/JnelyjwzKuZ1q/7l0D808Z7xFXhr9/x/p+uvqncPy1I+9feAa8769jvszzB57nP5/9T/wuP8/se4buaDd1vrrM/YeXCpumyVPb2zll0Qzczom7zmYHrp1zVsGOZ61/g1nW7uTjhPl1AKxr6WHVvJE7Z+XI8+NXf0wyneS5/c9NiQmu4zEpGddeKwtIzbzhgavDAY3LRp0sPFgqrIxrPvb17yPoDlLnrSvrcQRcmiosImMrd8b1l8BlZT6GqSUZg3s+agWtq99uZVezDbdxOOFN37X6Cl/6NXz7aPjd9XDHDfDdY63s2N+/C+vvhEe+BL+4HPoOTv7XU0rptDXtt2F4yWYsk3EtPHANJ8PD1+EA1MyBuoWw97lSHPX0d2Ad/P3bsPra7EFrAR7c/SABV4AfX/xjkmaSB3c/OOx+O+NqlwpvO9TPzvYQl2bZmVpKdiDROWOBdSGoY0fRr7Wg3k9dwK0BTQLAzp6d/GXnXzhv3nmkzBQPNI+cXD2dxFKxojOueU8V7rGygNQuGOhxHfLZbk8WzkGBa2H29e9jXlX2/diTye+2Mq5pU39vIpJbWQNX0zSfBDrLeQyTLp2GnY9b04H/+A9w32es0sRkHEId8Our4JVb4XWfsXoER1s7YBjw+v+Ad90BR19q/TA/tAkWn2cFvJ/dbf33/vugZ58VvI7jhLzs+vZDKjZsojBAPB3DLHCqMAz8oExk6XEFq891z5ojqww7m2Qc/vQR8NfD5d8Y98s19zSzpHYJJ808iSW1S7hv133D7rd7XO2M673rD2AYZN2ZWkqZjGvVwICSlueLfi3DMFg9r1YrcQQg8z3+lXO+wvIZy7l3171lPqLimaZJLBWb+KnCPS3g9EKwkVjqsF7xxuXQvdfquc/2Hk4HLoeh4Ux5sgPXcrN3q0eT0TIfiYhMZRrONJki3fC7d8Hup62VK4EGa6z/mh+DtwbcAau095pbYPXb8n/d5W+w/stl0dlww5/gtnfAzy6Gd94Gi84a71cz+eyr7EMC10Q6QcpMQtpdVI9re7x9ZI8rWIHr+j9Ya3EOC5RLrms3PPVdSAyUSTlccOqNMP/UiX3ffPz9O3BovfU9E6gf98vt6t3FGbPPwDAM3rjkjfzwlR/SGm5lZsCaENqXsEqF7YzrfesPcPriemZWF5fhyVeNpwaX4bJ2uXprYe/zcOK7in69E+bX8aMndhBNpPC5C/u+lMpyoP8Ajf5GGvwNvHHJG/neS9/jQP8B5lTNGfvJU0wynSRtpjPrxApVUMa1dh4YhlUqPDTj2rAMMK22kdnZO4y8LgcxDWcak2ma7Ovfx5lzziz3oWQC13AyTMAdKPPRiMhUVe5S4TEZhvFBwzDWGoaxtq0t9+62KS/SBb+6wjohftN34V9fg39ZB5/eCdf9Hla+xRrA9L57Cwta87XgdPh/D1vBx6+vtCbATje7/g6GE+aenLnJvjprmkVkXIf0uMaSadLpIdnVoy+xAshn/r+SHHpO+1+2Lia8+nurp3bvGmtq78/fYL33ZGd8UwnY+QQ8/CX46UXwxDesEuFj3zTulw4nwrSGW1lcuxiAc+edi4nJq22vZh7TG+vF4/Dgc/nYfqiP7a39vHH1xJ/gG4ZBva+ezliXdcFgb/EZV4DV82tJpU027u8t0RHKdHUofIjZAavU/fim4wHY07ennIdUtFgqBoDXOUol0CgK6nGtsbKA8WR6eBtI3cLBx+TgHfhMl9F1RjuJJCPMry7vRGEYDFzV5yoio5nyGVfTNG8GbgY49dRTp2/d5v2fg9bNVpB69MWDt7v9cMxl1n8TreEouOkhqw/2jx+wgthlF4/9vKli52NWAO4bnC6b+SFXZI+rvQ4HIJpMEfAM/JOYsdjKer5wC5zxYWhabq0i2vk47HnOWvuy8i1FD/ABYPtDcMd7rb+H9/4FZh478EV1W+uOHvy8lWV+03cLW8tjmla2uHs3nPkRaxL1aPa9BJvusbL/2x+0fjWcVgD3us/A2f9Y9Jc41O7e3QAsrlkMwPIZy3E5XGxo38Aliy4BoLm3mblVcwH4v8dew+N0cPmqie1vtTX4G+iMdlrfY49/HaK9w77XCjE4oKmbUxbNKOFRynRzKHyIZXXLgMFe6vZIezkPqWjjDVzznyrcAkteBzCyx9VehTZa4OpyqFQ4D/YqnKlQKhxwWVlWBa4iMpopH7hWhG0Pwqu3w/mfHh60lkOgHq6/E35yPvz5n+Ejz4JvGkw+DXfC/lfggs8NuzmTcU0XN1XYXocDEIkPCVzB+vt65Xa49W3QdCw0PwWJEBgOMNNw1Ovh/H+FhWcWFsCaJjx/s3UxY9ZKeNcfrIFQmQOrg2t/A4/9l7UjtWcfvP2X+QVR/a1w1/+z1igBrP0lXPY1WHHlyGMMtVuDu176jZVdDtRbJdLHvwOOuhC81SNefjyae5sBMhlXj9PD8hnL2dixMfOYrZ1bOb7peJ5+rZ0/vbKff75oGTNrJrZM2Fbvq7cC12OvBkyrz7XICzuza33MrPayXn2uRzTTNDkYOsg5c88BoNFvTXY/UgNXt8NNPB0f/UGpJPQdsEqFYeQ+5KpZ1mdw7/6cL+F1ObTHNQ9TZYcrKOMqIvkp9zqc24ELgEbDMFqAL5qmeUs5j6lokS64+8Nw0b/D7NWDtx94Ff70YWhaAed/qnzHN5TbB1f9EG65BP72r/DWn4wvcziafS/C+rtgwWlWEFBsMLTzccC01gMNkfkhZxaXcY0mo4OB6+EDmqqa4K0/soLMrmZYdbU16Xn+adYk58e/Br+4DJZeCNf+euzA0jStUuBn/j+rHHj55XDNT7P/mRgGXPR5qyzuL/8Cv3gjvP/e0S8ydOywpkn3t1pZ2pkr4G+ftjLsc0+GmrlW5tbpsY5l+wNWFvmsj1qZ1SKzi/lq7mnGwGBh9cLMbasaVnHfrvtIm2n6E/3sD+3nonlX8tk/rmNRQ4CPXLhsQo9pqHpfPbt6dsH808HhtkrTx1GRcPz8Wl7VZOEjWn+in0gywuygVTVQ7a7G6/QesYGry+EinMw+VCmj/6B1YbDWCqbiqTRVviGnKk4XVM2G3gM5X8Lrcirjmgc7cLWrXMop0+OaGOP7Q0SOaGUNXE3TvK6c719S7a9Bywvwk9fBMZdb04DNtDUx2FcL77x19AnBk23+qVb28rH/soZEveFr+QevOx611uwYDutr8tZYZbV1CwYf09NireNZ+3Pr98+Z1pTIpRdYQdLh+1FTCat0dvsDVtCw6CxYdc3w9/TWwtyThj3NDlyLzbjGUjE8A1W40WwL61e82frvcGd+yNqN+9KvrJLeX77RCl6zDXJqf83qFd35OIRawR2Ei78EZ/9z9lVHQ518A1TPhdveDo98Gd70neyP69xpBbfpBLz3r4N/vh98HNbeYvU0dzVDKm6tXAJrX+0lXx4sUZ5gu3p3Mbdq7rDBLsc1Hscd2+5gb99eWsOtAPzy8QSBZIqbbzh1Ugcb2RlX0xPEWHD6wMWS4h0/v45HtrTSF01Q7Sug1FsqxqHQIQBmBayp2IZh0OhvPGID17yGM/XYO1wHAtdkeuQO55q5Y/S4OtTjmoeWvhbqffVTYhiS362Mq4iMTaXCpbLgNPjHF6zSy51PWEGdwwnzToa3/F/m6vGUcv6/WiW4z/0Q6hZZwVgu3Xtg05+tjOHmP4N/Brj81nqaaA889yMrwDMcVhC1/2XAhNM/YAXIrZthy72w4U645WI49gor4AUId1iTlmO9VnAK8MJPob/NOqb9L1s9mEddaF1tH2Joj6vn8JObMdhXeJ1O60QqEi/wRMcTgDM/DA1Hwx/eCz84HVZcYQWmtlTM+nNzeWH5ZbDs9QNfe1X+73P0xXD6P1jTp49/h9WDOVT7dvjtNVZQ+v6/WZlWm9MFZ/yD9V+ZNfc0s6hm0bDbjms4DoAN7Rt4ZZ91Inp03XJ+dv15k1YibGvwNxBNRYkkIwSWXgCPfc3691HkNOXj59dimrBhXy9nHdVQ2oOVaeFQeCBwDQ6uc2rwN9AWmZ6DBjOBa5EXYfNahxMa+LMZWE0VP3wdDlitFW2j73LVVOGx7erZxYLqBWM/cBKoVFhE8qHAtZQC9fDm75f7KPJnGHDZf1srXx76D1h8bvb1Agc3wK/fAuF2a5/neZ+0+j/dA4FF9x546Iuw+xnrNesWWQHd6R8YnAC5+Bzrvws+Y2UOtz0wODHX7YfjroJj3mQFdhhWIHj/Z6yAtXWj1fd56VdHHJrd4+pyeAteoG7/oHQ4rBOpEaXC+Tr6YvjHtVbp8I7HRk4CXnGFldGuHseQoYv+3bpg8Is3Wn9G538aGo+GJ79pXTTwBOE9fxoetE4hpmmyu3c3J80cnjE/qu4ofE4fa/a9wt2v7sAIVvOrG17PjODkVyfYu1w7oh0Dget/wa4nre/NIhw/ZEBTtsD1h4+/xp1rWzjYG+V1y5v44PlLOWmhBjlVkkzgGhgMXJv8TZlBZdNNLDkJGddwh/VrwPo3E0+m8Y7IuM6DHY/nfAmf20kolscQqCNYPBVnQ/sG3nHsO8p9KICGM4lIfhS4HukMw8oI/+gcuOM98O4/Qv2Swft3PmEFkS4/fOS57IFR3UJ4+y/yez9frVXumqvk1fa2n8OjX7XWksxaBVf9aDAIHsL+IedxFJ6ds0tWHU5rWEjRgStYGYArJ3B1jrca3vdXa8rxujvgZ6+3/iyj3XDSu+H1X4SqmRP3/uPUEe0gnAyPyLi6HC5WNKzgrzsewvQ6OGnmirIErTAkcI10sGDuyeCptsqFiwxc64Me5s/wsy7LgKa1zZ188/6tnLZ4Bmcsref+DQd5fGsbD3/ydcyr84/jq5Cp5FDoEAYGTf6mzG2N/kZePPRiGY+qeHbG1eP0FPX8vDKuduDqt/49jhjOBFapcLwv5+Rvr8tBZ0gZV1vaTLO1cyvRVDRz8XBTxybi6TinzDxljGdPDmVcRSQfClwFgo1Wf+Zt11q7O499kxXQJiKw4S5r4fu77hge0E40lxcu/cqYD4umrIxrMYGr/YMSYyBwjU/xYR71S+EN/2X1CD/xDask+/xPWat5pji7f3VoyaRtpfddvGR+DsOT5MRZV0z2oWXUD5wod0Y7rRLrJedZvdWmWfTwsuPn17JuX/ew25KpNJ//0wbm1Pr45ftPJ+h18dELl3Hxd5/gK3/ZxI/fM/X/PiU/h8KHaPA34B6yzqrB30B3rJtEKjHs9unADlx9zuLK+N3OPPa4hjvAHbBaMciyDgcyO17pO5AjcNUeV1t/vJ/r7r0uM9X9d2/6Hcc1Hpe5eHLSrJNGefbkUcZVRPJRWFOgVK6FZ8AHHrXKT1972BqU1PyU1VP5gUcnN2gtwHgyrocHrlmHM01FvhorgL3u9mkRtAK0ha2+tZn+4VnhRCrNnc+4WJi6EYATmk6Y9GOzNfis0sTOaKd1w7LXW7tw27cX/ZrHz69jb2eEztDgCpBfPtPMloN9fOGKlQS91rXD+TMC/NNFR3P/xoM8trW1+C9CppSD4YPDyoSBTPa1I9pRjkMal3FnXI089riGOzNlwpAj4zrGLlftcR304O4Hae5t5lOnfop6Xz3feOEbmKbJS60vsaR2SabSpNzcTjcuw6WpwiIyKmVcZVDDUXDTg+U+ioLYgauviJ4rO3A1HSUoFZZR2cNomgJNw25/cOMh2vtjfOvcd7B60XWZ4LEc7BO4TOB69BuAT1qTrpuWF/Wax8+3ho29tLuLi1fOYld7iG89sJXXHzuTy1YN73n+f+ct4Y8vtfDvf1zPAx8/X5OIK8Ch0KFh659g+C5Xe03OdDFpGdchA9GyD2caWN+SY5er1609rrZ7XruHJbVLuGHlDVS5q/jPZ/+T27bcxsutL3PpokvLfXjD2LvVRURyUcZVpjXrh5yBp4gpl5nAlWlSKjyN2RnXBv/wwPS3z+1m/gw/5y9votHfWPCArVLyOD1Uu6vpiAxkwuoWwMzjrEFiRTp54Qxm1Xj530e3E02k+PSdr+JxOfivt64e8bV6XU6+9fYTONgb5Wt/2zKeL0WmiEPhQyPK4+3A1f43MZ1MTsa1I5NxTadNEilz5MT4TMY1+y5Xr8tJTBci2d27m5daX+ItR70FwzC4atlVnDrrVL7+/Nfpi/dx8qyTy32IwyhwFZGxKHCVaS2cCOMwvfhche/7tAPXFNbJWDiuKZQTpTXSSr2vHrdjMIu4o62fZ3d28K4zFuJ0lC9gHareXz+YcQVY/gZrWnaku6jX87mdfPbyY1nX0sPl3/87LzR38dWrVjG7NnvG6uSFM/jAeUu5/fk9PL+rM+tjALpCcdr7Y8P+S6XNnI+XyRdOhOmL940oFc5kXKPTb5erPVV46C7mQuSfcbUC10TaypqOyLi6fRBoHKNUWBnXP+/4Mw7DwZuPsnaROx1OfnrpT/ns6Z/l5Jknc87cc8p8hMP53QpcRWR0KhWWaS2cDOPAN/LEJg924Jo0Y3icfvq0PmHCtIfbh01WBbjnlf04DHj7KVNjjyBYfa7Ddmwuvwye+q7V9736bUW95lUnzuM3z+7mpT3dfOWqVbzlxHmjPv5jFx/NX17dzxfu2cBf/+lcXEOyTaZp8m93r+f25/eOeN7yWVX8+sYzcgbFMrnsHtbDqwzscvj2yDQMXEswVTiZTmKaZu7qiiE9rvGB4DPrju6aOblLhQcC11Hf5wiw5sAaTmg6gZmBwdkCLoeL61dcz/Urri/jkWUXcAUUuIrIqBS4yrQWSoQwTC/ecWRcI8kI1b5q+qMKXCdKa6R1RH/rfesPcPqSepqqy7P+JpuZgZls7tw8eMP8U60Jpi/9qujA1TAMfvKeU9nZ1s8ZS8fu4Q14XPzHFSv58K0v8fE7XuWspQ14By7MvNDcye9e2Mt1py9k5ZzqzHMiiRT/+8hrvP0nz/DO0xZy0sI6zj6qsajjldLoiVlrkGZ4h+/mdTvd1HnraA9Pz8DVaTiHVU4Uwn5e0kziNrK8RioBsZ6RgWu2C5PVc62pwll43dbPg3gqXdTPhkqQTCfZ0rmFa4+5ttyHkje/y084qeFMIpKbAtdJEkvFuHPbnVy7/NpptwJhKrMD12Iyrna5WyQZocrnok+B64RpC7dxbP2xmd+/1trH9tZ+3n3mcWU8qpGaAk080fLE4A0OJ5z+QXj4i3BgHcw5PnNXV7SLaDLKnKo5Y79utbegAP2yVbO57vSF/Onlffzl1eFZpfedvZgvvnnliEzSGUsa+KfbX+ZbD2wF4I2rZ/Pvb1qpvbBl0h3rBqDWWzvivkZ/47TNuBabbQUr2wdYq4CyBb/hgfL4geFMdrmvN9vne7ARDm3I+j7242PJIzdw3dG9g1gqxqqGVeU+lLz5Xf7MBR8RkWwUuE6SR/c8ytef/zr1vnouX3J5uQ+nYoQTYUh78XoKD1y9Ti8GxkDG1UVfdIzeKylKKp2iI9qR6e0DuG/9QYARk3XLbaZ/JpFkhFAiRNAdtG485X3wxDfh2f+Dq3+SeewXnv4C69rXcc9b7qHOV1fS4zAMg/++ejVfvWoVB3ujpFJW/6rbZTCnNnsgesKCOp789IX0x5L86plmvv/Idh7ceIi3njSPr7511RF7Al8uXdEuAOq8dSPua/A3TM8e11Ss6InCMDzjmlV4YDDaQMY1E7i6s3y+Bxog1J51z7KdcY0l0nCEVs5v7NgIwHGNU+vi4Gj8Lj8HQwfLfRgiMoVpONMk2dxhlR8+vvfxsh5HpQknw5jp4jKuhmHgd/mJJqNUe930q8d1QnRGO0mb6cwOV9M0uXf9AU5ZNINZNVPrrNIuZ24ND9ml6q+Dk98DG+6ELX8DIJFOsObgGjqjnXznxe9M2PE4HQbz6vwsbAiwsCGQM2gdqsrr4qMXLuOxT13Au89cxB9ebOEHj742Ycco2WVKhX0zRtw3wztjWmaWSplxzWpE4GpNBs560SXYCKkYxPtH3DWYcT1yJwtvaN9Atbt6xDqmqUxThUVkLMq4ThK7b+6pfU+RTCczP8BlfEKJEOlUU/ZSsjz4XL5MqfDeTvXWTAR72FFjwMq4vrSniy0H+/jKW6ZeJsAeINUWbmNJ7ZLBOy74LOx9Hu54D6y+lo1mmEgywgpvE3967U9cc/Q1nDjzxPIcdA7z6vz855XH0RtN8KPHd3DZqtkcN3ewbLWjP8YPHnuN+zccHDGR+JxljfzPO04s6P3SaZOHNx/iF083c9zcGj5/xcpSfBnTVlesC4fhoNpTPeK+Wm9tppR4OoklY0VPFIYhGddcK3EOD1wHdrH6smZcByo4Qu3gHf5nPLRU+Ei1sWMjKxtHthRMZQG3hjOJyOiUcZ0EpmmyuXMzTf4meuO9vNz68rhfM22m6Yp2HfEf8uFEGDPlKSrjCoNXeKvV4zqmnnCCLQd72ddd2Pecva/Szrj+/Olmqn0urj55fsmPcbwyGddI6/A7/DPghntg2SWw41HWHHwewzT5n9deAWBty1OTfKT5+8IVK5kR9PCeW57nrhdb2Hyglx88up3XfetxfvVMMycuqOP1K2Zm/jtmdjV3v7yPF3d35f0er7X28c6bn+ODv3mR9ft6+NlTu3hsa+vYTywB0zQ50DP1Pgd7Yj3UeGpwGCM/m2q9tfTGekmb0yuwKlnGNddKnFylwrkyrkOfM4T9+OgRuss1noqzrWvbtOpvBWVcRWRsSvtNgoOhg/TEevj4KR/nBy//gCf2PsFps08r+vV29+7m2r9cSzgZZkntEu55yz3T6qpqKYWTYVIpT9H9e/YPylqvelxH09ob5dLvPUl3OIHTYfCXfzyXlXNr8nvuQBDYFGhif3eE+zcc5KZzlxD0Tr2PH3tthB1sD+OrgXf9DoDnH7iJY+O9zFv5Mao3fo+29bfDCR8C59T7muoCHm7/wBl86g/r+OQfXs3cfunKWXz6smNYNnN4tioUS3LONx7lh4+9xi3vG/tzakdbP2/5wdO4nA6+fvVqrjxxLm/5wdP82x/X8+d/PDfvoVRPbW/n1ZZuPnLBUXl/nt3xwl5+8Nhr7OkM89WrVvHuMxfl9bzJ0B3rztrfClbfq4lJX7wv6/CmqWq8Pa524Jo74zownMlvD2eyS4XHyLgexu6JPVIzrtu7tpNMJ6dVfytYP4+jqSipdAqnQz35IjKSMq6TwC4TPmXWKZw862SeP/j8uF7v/l33E06GuWLpFezq2cWmzk2lOMxpJ5VOEUlGSI0j4xpwBaweV5/V42qa5thPOgJ9+8GthGJJvvW24wl6nHznwa15P7c93I6BQYO/gVvX7MY0Td4zhQKMoYLuIAFXYHiP62GiySivtL7C6bPPgNM/QJO/ibbQQXjoC5N4pIVZNrOauz58Nr+68XR+dP3J/PWfzuXmG04dEbQCBL0ubjxnCY9saWXT/t5RXzeaSPHRW1/C43Lwt4+dxztPX0jA4+Jbbz+Bjv44b/jek9y/IfvKEoCN+3v42d938h9/2sC7b1nDtx7Yyr3rcz9+qK0H+/jc3euZEXBz0sI6/uvezexqD+X13MnQHR09cAWmXbnweDOudqnwqBlXbw24rPewS4WzZ1wHVktlWSuUKRVOHKGBa/d2AI6ZcUyZj6Qw9oq6aCpa5iMRkalKgesk2Ny5GYfhYPmM5cyvmj/qSXE+Htv7GMc3Hs9nTvsMTsPJQ80PFfT8LQd7+efbX6ajPzau4yg3u6QomfSMu8e12ucibUI4fmSWlo1mw74e/vBiC+87ezFvP3UB//C6o3hkSytrmzvzen5rpJUZvhk4cHHXi/t43fImFtQHJvioizczMDPTl5vN+vb1xNNxTp9zOgBN9ctorZ0Lz/0ftKydrMMsmNNh8LrlTVy+eg6r5o2e5XvvWYup8rr40RM7Rn3c1+/bwpaDfXz32hOHrd05cUEdf/3nc1kww89Hbn2JJ7cN//NMp03+77HXuPIHT/PVezfzm+d2887TFnDs7Gq+cf+WMYfqmKbJf9yzgWqfi1+8/3R+dP0puJ0Gn7zjFZKpqRGsjJZxtbOs021A08RnXDsyq3BgrKnCo2RcBwLdI3U4087unXgcHuZVzSv3oRRk6G51EZFsFLhOgi0dW1hcsxi/y0+9r56uWBepdHE/UA+FDrGxYyMXLryQOl8dp88+nYd2P5R3pjCWTPGx21/hz6/u56v3bi7qGKaKUGIgu1LkVGEYLBWu8lknVOpzHem3z+2myuPiHy86GoD3n7OYxiov33xga17fd+3hdpr8Tfx9exsHe6Nce+qCiT7kcWkKNGUvFR6ws3snAMtnLAes3t02jw+CM+GBf7fWc2QTD8HT34edT2S/fwqpDbh595mLuHfd/pxZzOd2dvDLZ5p539mLufDYmSPuXz6rmts+cCbLZ1Xzj7e9xGutg9Nff/bUTr71wFYuXzWbNf/2ejZ/+TK+fs3x/NsbV7C3M8K7frqGbz2wJWeP4h9f2sfzuzr59BuOpT7oYXatj69ctYqX9nTzkyd3luYPYZy6Y9051yTZgasyrocJd2T6W2GwRzXrhUlPEFy+rBlX3xFeKryjZwdLapdMu3LbTOCaUOAqItkpcJ0Er3W/ljnJbfA3kDbTRZ+w2Ot0LlxwIQCXLL6EPX172Na1La/n/+8j29l6qI/zjm7k7pf38fftuU/Qp7pQ0jqhNtPecfe4VvusEyr1uY708p5uTl40g1q/9WcU8Lj4p4uW8fyuTp7cPvYuys5oJw3+Bv6wtoUZATevXzFrog95XJr8TaNWRTT3NuN3+ZkVsL6OpkATrdF2zAv/DfY+BxvuGv6EdBo23QM/PNMqJ/71lfDHf7AC2SnspnOX4HY6+PHjI7OukXiKz9y1joX1AT59We5yxKDXxU9vOBW308E7b36OrQf72Hqwj28/sI03HDeL/++6k5hV48Pvsf79nr+8iY+9/mgi8RT/99gOfpjlvVt7o3zpLxs5ddEM3nHa4EWQK0+Yy5tWz+F7D28bs8R5MozV4wrKuI5wWOA66nAmw7CyrqHcw5mO2MC1ewdL65aW+zAKFnBblTjhpCb8i0h2ClwnQUe0IzOttMHfkLmtGI+3PM7C6oUsrbV+KF204CIAnmx5cszn9seS/PTvu3jrSfP46Q2nsqghwPcf3l7UcUwF9lVZM118j2vQHSSUCFFtZ1yn6S7XdNrkG/dv4bfP7S7p6/bHkmxr7ePEBXXDbr/u9IXMn+HnWw9sGTPr2hHtwO+o5aFNh7jqpHlF/11NlpmBmbRH2nN+Xbt6d7G4ZnFmgNDMwEyS6STdK98M806BP33YClQBDm2Em18Hd9wALj+8509w/r/C+jvgV2/OWuY4HqWcUttU7eWdpy3gzpdaeH7X8LLwbz2wld0dYb5xzfEEPKMPpFpQH+D3/3AmTge84XtP8obvPUmN38XX3ro66xCmj1+ynL997DyuPGEuP35iB81DMr6mafJvd28glkzzzbcdj9Mx+HzDMPjqVavwu5385MnRS5wnWiQZIZaK5Ry8VOuZvqXCE5pxjXRaE7zt9xttOBNYfa6j9rgeeaXC4USY/f37Oar2qHIfSsFUKiwiY5naZ5AVIJqMEklGmOG1fhg3+AYC10hxgetr3a9xQtMJmRO+Bn8Di2oWsbFj45jPfWTzIeLJNNefsRCf28kbjpvNun09xKfpVemhpcLF9rhWe6rpT/RT7Z3epcLfenArP3p8B//9t80lyRq3R9ppC7exrqUb04QTF9YNu9/jcvAvFy9nw75eHt48es92Z7STTS1pHA648Zwloz52KmjyNxFNRelL9GW9v7mnmcU1i4c9HqA12gHv/iPMPckKVH98Hvz0Iug/BFf9GD78DBx1IVz0ebj2N1ZQ+8srBiepjtOe3j2cc/s5rDmwpiSvB/DJNxzDovoAH7n1RR7f2sozO9q568UWfvHMLm44axFnHdUw9oswOBzq4xcv558uWsavbzyDhqrRpw3/+5tW4HE6+MKfN2YuIjy8uZWHNx/iE5csZ2lT1YjnzAh6ePMJc3lg48GyVk/YAan9uX+4ak81Bsb0KxWe6D2ukZ7DAtdRelxhIOOqqcJDNfc2Y2JyVN30DVyVcRWRXBS4TjD7xGSGbyBwHci4dkYLP1lNppO0hduYHZw97PaV9SvZ1DH2ZOG/rT/ArBovJy+0juXEBXXEk2k2Hyh/WV0x7B9u5jgD10gygm8giTAdS4Uf29LKjx7fwXlHNxKKp7j75X3jfs1PPP4JbnzgRl7aY11gOXF+3YjHXHXiXGbX+Pj1s805XyecCBNJRmhuNfiXi5dP6aFMNrs6IlufaywVY3//fhbXLs7cllmhE2kDfx2852645Mvg9sPRl8CHnoITrxu+KmfFFfCuO6BzJ/z2GoiO/9/gbVtuoz/Rz5bOLeN+LVuNz81P3nMKkXiK9/3iBd710zV88g+vMn+Gn89cdmxBrzV/RoCPXXw0n7z0mLxWKc2q8fGpS5fz5LY27nyxhWgixZf+spHls6q48dzcF0Dedsp8ook0f8tzOvFE6IpaO3BzlQo7HU5qvDXKuA6VTkGsB4b0BY86VRisXa5ZM65H7h7XHd1WtYFdlTWdBFzWzwf1uIppmty9/W4+/tjHCSd0IUMGTb2lgxUmE7iWIOPaHmknZaZGBq4NK7mv+T66ol2ZAPlwoViSx7e2cd3pC3EMlNedNJBFe3lPFyccVgo6HdgZ1/EGrgAOp/WDsr8MGdf+WJJfP9vMMbOqOeuohjFLLw937/oD1AXc/OJ9p3HNj57hV880854zFxW927cr2sUrra9gYvJY76MsbjiWGcGRJ6sup4Prz1jIdx7axs62/hEZsL5ogn+56+8AzKlq4qZRgo2pxM6gHgofGpG12NO7BxNzeMb18EDXE4RzPmb9N5qlr4NrfwW/fzf85iq4/s7Biaqxfnjp19C1y3q9s/7ROknPoT/ez59e+xNgfU6U0tGzqnn0UxcMG9K0Yk7NpOzhveGsxfxt/UG+/NdN3PLULlq6Itz2gTNwO3P/ez9xQR1Lm4Lc9eI+3nHawgk/xmzsz/3RdrTWemqnZeA6YT2u0YE/C3/d4PslU7idxrCS8GFy9rgeuRnXnT07cRkuFtRM7SF42dg/j3vj0/NiupTOp5/8NPc33w/A1Yeu5rz555X5iGSqUMZ1gtmZVXu6ZI2nBrfDXVSP68HQQYCsgSswatb10S2txJJpLl81+Nw5tX5m1/h4eW93wccyFZRiqnCNx8r8pAzril45SoW/8pdNfPP+rdz0q7Vc9X9PF7TCwTRNntjWxnlHN+FyOnjv2YvZ0Rbi2Z3FlaIDPLP/GUxMqt3VbI/fwwnzc598v/P0hbidBr85rLfWNE0+ecerPLljFwCfv/y0UYONqcReIbGvf2Tmurm3GWBYxjVTKlzMmqtjLod3/BYOrreGN915oxXIfv8EeOBzsO4OePp/4ZZLoXNXzpe5Z8c9hBIhvE7vqKt8ijWrxseZSxsy/9mDuiaaw2HwjbcdT43PTY3PzX9fvZqzj8odwIPV6/r2UxbwfHMnz+wobRCfr8MrbbKp89ZNq1LhZDpJykxNXMY1YmWph2Vck+nRB+8FGyERgsMydEdy4LqjeweLahZl/qynE/tCjwLXI1tfvI/7m+/nqmVXYWCwvn19uQ9JppDpcSY5jXVHu4HBExjDMKj31ReVcc0VuB7bYJXsjRa4Prallfqgh1MX1w+7/aSFdby8p7vgY5kK7AEOZtpT9FRh+wpvygxjGJM/nOmJbW38fu1ePnDeEr71tuPZdqifH2WZpJrL5gN9tPXFeN1yK3i6fNUc/G7nuMokn9r3FDO8M7hx5T9hevbR0Lg352Obqr1ccfxcbn9+DzvaBted/OiJHTy46RDXnmF938+tGrkuZaqaFZyFx+Fhb+/Ir7u5pxlgWMbV4/RQ560rPmA85nJraNPCs2DPGmjdAovOhpsegs/uhvffZw2t+dWVg1mpwzy29zGWz1jOivoVo67ymY6WNAZ5+rMXcceHzuK60/PLoL7v7MUsaQzy6TvXESrDwLW8Mq7eWnri0yfjGktZe78nLuPabf16WMZ11GqaYPZdroZh4HE5jrg9rqZpsqVzC8tmLCv3oRSlyl2F03BOu0oEKa3tXdbQ0EsWXcJRdUexrn1dmY+oeAd7onzoNy/y4u7SzLIQBa4TritmXUUeOqSjwd8wrozrnOCcYbfXeGpYUL0gZ+BqmiZP72jnrKMaRpRcnbSwjj2dYTr6YwUfT7kNZlyLnypsZ1xDyX6qPK5J7XHtiyb43F3rWDazik9eegxvP3UBbz5hLj98bEfO3ZmHe2KbFaScf7R1Auf3OLnw2CYe2HiIdDq/3b5Dpc00z+x/hrPnnU0DZ2CaBknv6HsxP3v5sfjcTv7ld6/Q1hfjfx7axjfv38oVx8/h5CXWiWq9r37U15hKHIaD+dXz2dO3Z8R9zb3NzAzMzKxtsDUFmjgUPlT8my4+xyob/sRG+Ke18I7fwILTrfsWnmGVEffug3s/lfXp3dFu5gbnWjtoI21W9uqp/4H7PgtPfNMqPT6C+D1OvvW249nXHeG/75v8fdV5B67T6ATdDlwnLuPabf16WI/rqIFrYCBwzTFZ2O6RPVLs7t3NgdABTp99erkPpSiGYVDjmX6931JaW7u2Atau9NWNq9nQviGvnfFTTUd/jHffsob7Nx7kI7e+NC3Ps6ciBa4TrDvWjYGRCZDA6nMtJuN6IHSAoDuYyRIOtbIh94CmHW0hDvXGOCdLid1JA4OaXplC5cJtfTH+tv4Aj245RCSe+4p5KBHC4/ABjqJ7XO2/l954L1U+16SWCv/3fVs42Bvlm287Hp/byhj/xxUrSKbT/PGllrxe44ltraycU8PMmsEsyGWr5tDWF+PFPV0FH9Pmjs10Rjs5d965bN4fw0w00JkYPXCdVePj61evZv2+Hk77r4f5/iPbefsp8/mfd5yYKZWv90+fwBVgYfXC7IFrTzNLakb26s70z5zYTOf8U+F1n7HW6Gy8e8TdvfFearw1NPmbaO/bD987Hh7+T3jlNnjsa/DzN0BXaVclTXWnLq7npnOW8Nvn9vBUHvuGS6k72k21u3rUcs3pViocSw5kXMcxVbjQjGs0mcbrHqNUGLL2ufrczoosFd7etT3nz/pnDzwLwFlzzprMQyqpWm9tSUuF79t1H//06D+V7PVk4m3r2kaNp4ZZgVmsblpNT6yHvX25K7+mqi/8eSN7O8N8+S3H0RVO8Ok7p2/meCpR4DrBuqJd1HprcToGf/iOJ+M6OzA7633HNRzH/tD+rINZ7D6vc5aNXF2xck4NhgEb90+dnpKv/HUTH7n1JW785drRJ9Ymw3gc1vj8ojOuXitw7Yv3Ue1zTdpwpr9vb+O2NXu46dwlmSnPADOrfayaV8uanWOXlUQTKV7c3cV5y4dfkLjo2Jl4XI6iyoU3d1rZqZNmnsQre7qpMRazbeDq52guWzWHX7z/NL505XH86PqT+ebbjsftdNAZ7aTKXYXXOfrqk6lmQc0C9vbuHXaVN22m2d69naV1I6d1NgWaJr5E97xPwpwTrCzqYVOI++J91HhqaOraQ186RmT2KvjQ0/C5PfDuO6F7L/z26iMu8/qpNxzD0sYgn7lr3aRWU3THukfNtoJ1gh5KhHLvNJ1iypNxHaNUODDwMy1XxrXCSoXTZpqPPvJR3vHXd/DV576a+TuxPbP/GeZXzZ+Wg5lspZ62ffuW23l87+MlH1onE2db1zaOqT8GwzA4vvF4gGlXLpxOmzz9WjtXnjCXG85azEcvWMYjW1rZ362J2eOlwHWCZZv0W++rpzPaWXDpw8HwwRH9rbbTZp8GkHWH49OvtTOvzs/CLKtIgl4Xi+oDU2olzou7u7jwmCYaq7y81pr7RNvKuFqB63h7XHvjvVR5XfTFJv4ksrUvysd//2qmRPhwZy5t4JW93WOucljX0kMiZXLaouHZzCqvi/OPbuK+9QdJFVguvL9/Py7DRb23iXX7ullcs5wDoQOZXu3RXHjMTN579mIuXz0nM9G4I9oxrcqEbQurFxJNRYf1rTb3NhNJRjLD0IZq8jfRHm0nbU5ghsfpgjd919oL+/jXMzen0in6E/3UhNpp3HAPAO1v+f9g9irrAcsuhnfeCh074P7PTNzxTUE+t5Nvvf0EDvRE+NrfSrcmaCyjTXi32YHtdCmLLE+P6xilwpmM68iLRlbgWlkZ15cOvcSB0AFOm30av9/6e37y6k8y9yXSCV44+AJnzZ2+2VYYmLZdot7vrmgXr7a9CsBr3a+V5DVlYqXNNNu7trN8xnIAjqo7Cr/Lz4b2DWU+ssLsaOunO5zgtIG5MpcNDEZ9cltlzaAoBwWuE6w71j1iCX2Dr4FkOllwOczBUO7AdUX9Cqo91SMC11Ta5NkdHZyzrCHnepSVc2vYNEUC1/b+GPu6I5x9VCNLG4Ps7si9vyucGH/G1ev04nF46I33Uu1zl7xUuKUrzC+e3sVn71rHJ+94lU/e8So33PI8/bEEP7z+5EyJ8FBnLKknnkqPOTTrpYFSYHut0VBXnzyPg71R/r69sA/Jff37mBWcxY7WCNFEmlPnWMHP1jyyrtl0RjunbeAK1vob2+YOKxudLXCt89aRNtP0xfuKer8Hmh/gU098io0dG0d/4PxT4ZT3wXP/Z/WwmmbmPas33cvMgf73tsRh/56XnAfnfQJe/i08/9OijnG6OmXRDD5w3lJuf37PpJ00dMXGDlztHa/TLXAdV8bVOUbG1em19h/b75lMjX5R0lsDDveI4UxgXcyMVdge13t33Yvf5ecHF/2AK5ZewS83/jIzRG5923pCiRBnzz27zEc5PqXs/X5q31OZi4n2wB+Z2lr6WogkI5nA1eVwsaJ+Bevbptdk4RearfOzUxdbPweWz6pido2PJws8J5ORFLhOsK5Y14gl9A3+wne5xlIxOqOdOQNXp8PJ6bNP57kDzw3L5L6yt5veaJJzj27K+dorZtewuyNMfxmmbx5uXUs3ACcsqGNRQ4DmjtxDisLJMC7DuvpfbI8rWKVJvbHekpcK90QSvPWHz/Clv2zioU2HeG5nB8/t7CCaSPHtt5/A8lkje5XB6s0zDHhujJU2L+3uYnFDgIaqkWW4F6+YRX3Qwx1rC+sL2d+/n3lV8zI9z5cuOwWALZ3FZauma+Bql9oN7avZ1LEJr9PL0tqRpcJ2yXmxvVn37bqPB5of4J1/fSeP7nl09Adf9nVYdY3Vw/qXj9Hba/VD10R7abzoPwGyTzi+4HOw/HL426fguR/DNBx2UayPX7Kco5qC/Mc9GwquQihGZ7RzxAXLwx2RGVfDyrhmDVyj3cOyrTCQcXWP8tluGFbWNVupsNtBtIKGM8VTcR5ofoCLFl5EwB3g46d8HJfDxVfXfJVQIsT3Xvoefpef0+dMz8FMtlpvLb2x0lxIf3zv4zT6G6n31StwnSbsi+THzBisRju+6Xg2d24mnoqX67AKtnZ3Jw1BD0sag4A1eOy8oxt5ans7yVTlfC6Vw8RvkD/CdUW7MjX6tkzgGu1gKSNPgrM5FLImluYKXAHOnHMmj+x5hL19e1lYY2WMHt58CJfD4HWjBa5zrJPuLQd6R6zLsaXTJn9+dT+3PLWLy1fP5sOvOypnBnc8Xtnbg8OAVfNqeKE5yB9ebCEcTxLwjPxWDSVCuBhfxhWscmG7x7W3hIHrtx/YSkd/jDs/dFbOP9dsav1uVs6pYc2u3IGraZq8tKeL85dn/3v1uBy89aR5/PrZZjr6Y1mD22z29+/n7Hln88rebuqDHlbNnsOswKxM72uhOiIdnNB0QlHPLac5wTm4DNewAU2bOjZxzIxjMuWOQ9V6xrd/sDXcyolNJ7KlcwsvHnqRixZelPvBbh9c/TOYsRj+/h16t9wNs+qoOeVGmhadC8+Rvd/W6bYmF99xg1UyvOlPcPk3rL7ZCudzO/nUpcfw4Vtf4sGNB7l89Zyxn1Qk0zTpinaNecHGDlxLNaBpXds61h5ai9fp5foV15fkNYcqRcbV6XDiMBzZS4Uj3cP6W8GaKuwL7iOeiud+30Bj1uFMAY+TcLz8F2NL5dn9z9IX7+NNS94EwMzATD516qf4ynNf4dI7L6U33su3zv/WsEGQ01Gtp5a+RB/JdDLrZ22+EqkEz+x/hjcsfgMtfS0qFZ4mtndtx8AYNktideNqEukEWzu3srppdRmPLn9rm7s4dfGMwfPk9tf4UOwXHJM8SMcfH2BWdfEXAEvCE4CLPl/eYyiSAtcSMU2Tjft7WTazKlP+aZom3dHuESVjDb7CM665drgOdcacMwB47sBzg4HrpkOcvqSe2kDu6ZYr5lo/6DaPErh+/f4t3PzkThqrvHzz/q1sOdDH9995YsmD11f3drN8VjUBjyvTk7u7I5wJrocKJ8I4Det4x5Vx9dTQF+9jsc896gCXVNqkP5oc9c/S9urebn67ZjfvPWtxQUGr7cylDfz2ud05S+X2dkZo748PG+x0uHectoBbntrFr57dzScuWT7me8ZSMVojrcytmssfdndx0oI6DMNgRf0KtnQUnnFNpVN0x7oz3+/TicvhYl71vEypcNpMs6VzC29a+qasj7czrsVmzw6FDnH2vLPpjHbmN+TJ4YDXfwFmLKb3ue8CKWpWXEWdtw6Xw5V7p6zLC++8zSoZfuTL8JPXWdnbWSvh2DdD09jfJ9PVpcfNZmF9gJ88uZPLVs2ekAtvQGbg0mSWCvfEenj3396NiZVNvmLpFWMOhypUKaYKg5V1zTfjGk51sZcv8YdtkdzBeLAha8Y14HHR0T99MjRjsateTpl1Sua2a4+5liZ/E59/+vPctOomLltyWbkOr2SGDkwc69/QaPb27aU/0c8ps07B7/Jz1/a7SJtpHIYKDaeyg6GDNPob8bsGWwZWN1rB6vr29dMicD3UG2VPZ5gbzlo0eOPLv+Go137BTKcfzxYHOMv8fRiYocD1SPf0ax28+5Y1/OJ9p3HhsTMB6E/0kzSTI0qF7Q9je8drPg6GBwLXHFOFARbXLGZmYCZrD67l2mOuZXdHiO2t/Vx3+sJRX3turY9av5tNB7L356XTJne/vI+LV8zi5veckgliP3HJchYPlEGUgmmavNrSzWXHWV/j4gbrtXd3hHIGrrWGlUn0jONDoNpTTVe0i2q/i1gyTTyZHpHB7QzF+dBvX2RdSze3vPc0zlk2crWQLZlK8293r6epyssnLy0uEDhjST23PLWLV/f2cPqSkYGv3d86WuC6fFb1wF7Y17h05SxWzRv9RPZAvzWFuMY1i53tIa49zSqXPXrG0fx9398LvgLeE+8hbaanZakwwILqBZmMq30SlK2/FcaXcU2mk7RH25kZmElToInWSGv+Tz75BnrrZ8IT/0qNpwbDMKxBUaNN0HQ44ZT3wsq3wBPfgFduhQ13wlPfg3f/ERacVvDXMB04HQb/77wlfOGejby4u6uoC0r56IoO7O4eazjTOLP0Qx0KH8LE5JJFl/DQ7odo6W8pfeBagowrWH2uOTOu1cMz4RHHbiCd6S/PKtCYddVTwOMkUkE9rs29zcwKzBqxQ/rChRfy5Pwnh20umM7sjHFPrGdcgav9GTgzMJNEOkEkGWFf375pPXH5SNAWaaPRP/z8anZwNo3+Rta3T48+17WZ/tYhP2MSEfDV8e7q23Aa8MePnFOmo5v+dOmpRE5bMoOgx8nDmw9lbrMnsR7+4WtPsj3Y183Pn9rFLU/tYvcovZwAnRFrPcrh/6CHMgyDE5pOyPzjfnizdQJ88YpZo762YRismFOdc0DTun09tPXFuOL4OTgcBleeMBeA9ftK25u1pzNMdzjBCQvqAFjYMJhxzSaUDGGkvfjdznFlT+yMa2O1FQQf6o0Ouz8ST3HNj57hlb3dzKrxcdOvXuCF5tzran7z3G427u/lC29eSbVv7OxsNqcvGb3P9aU9XQQ9To6Znb1P1vaVtxxHfdDDJ+54ZdSduGCVCQP09FYBZKbhzauaR8pMcSh8KOdzs7G/Z6fbDlfbqsZVbO3cyr7+fWxst4YmrahfkfWxmR7XInqzOiIdpM00swKzitoHa7+n/bnS5G+iNZxH8Ouvg8v+Gz67Bz62zlot8purYOcTYz833AlPfBMe+Hdr2FNqepRkvu2U+QQ8Tv748j7AyqQf6D/Atq5tJXuPztjA9/0YF2yC7iBOw1mSjKt9km5n41r68tsDXYhS9LiCVc2QSOWXcY07rB7zUcs8g40QzlUqXDmB6+7e3SyuXZz1vkoJWmFI7/c4JwvbKwcb/Y0sq1sGwPZu9blOde2RdpoCw1ugDMNgdePqaRO4vtDcic/t4Li5QxIuyQi4/bzu6EZe2dtNT3h6rEGbihS4lojX5eS8o5t4ZHNrZjiSnVE9fEiHx+HB5XDx6LY9fPmvm/jKXzfxlv97mpf35M7AdsW6cBkugu7RM5yrGlfR0t/Cof4Obluzm2NnV2cCwNGsnFPLlgO9JLI0jT+06SBOh8EFx1gfJstnVeNxOthQ4sB1Z3to4PWtwKnW76Y+6KE5S+BqmibhRBjSXgKe8f3QrvZU0xu3yryBESt4ntjWyq72ED+47iT++OGzaQh6+fYD2afshmJJvvvgNs47upE3jaOPri7g4ZhZ1Tn7XF/c3cUJC+pwOkYP2OsCHr719hPY3trPv929ftQVTPtC1sn87kNefG4HqwcytPOr51v39+0r6GuwTxymY6kwwNXLrsYwDO7Yege3br6VJn8Ty2Ysy/pYO0tQTPbMDjLtjGtbpK2gVVn2e9rBc1NgjIxrNjMWwfvvg9oF8NtrYMNduR+7+S/wg1Phsa/B2p9bw55+dQX0jP790Rvv5cmWJws7rhILeFxceOxMHtxorYp6z33v4dK7LuWaP1/D1s7iJmcfLnPBcozhTIZhUOOpKUnG1W47ObHpRGBiA9dxZ1wdbpJmtoxrz4ge16TL+jp2dO/IvWoq0AixXkgO32nqd7sIT9LAwYd3P8xjex6bsNc3TZPmnmYW1yyesPeYKko1tMz+DBwWuGpA05TXFm6jyT9ydsfxTcezu3d31u+LWzffygPND0zG4eVl7e5OTlowA/fQSsBkDFxezl/eRNqEp3dor3CxFLiW0MUrZ3GwN8rG/daJSK6SMcMwqHJXsbOjg7edMp+HP3E+tX431/9sDQd6si8n7on1UOerGzOzuKrBWl/ynSceZkdbiM9cfmxex37yojpiyXTWfa4Pb2rltMUzqAtYJywel4Nj51SXPON6oNvKdM6pHextWNQQyJqNjiQjmJik0x784wxc7YzrUQNlz4cHrvdvOMiMgJuLjp1JQ5WXd52xkDW7OtmTJaC+d90B+mJJ/vn1R4+7h+7MpQ28uLuL+GG7CMPxJFsO9nHKovzKqF63vImPX7ycu1/exzt+8hwfve0lWrpGHvu+vn24DBcb9sJJC2ZkyqXnVc2z7u8vLHC1TxzsYWTTzZyqOVww/wJ+vfHXrGtfxz+f/M+4Hdkz6D6XD6/TW1TGdWjgOjMwk0gyQn8i9/7iw/XF+3A73JlMWKO/MXeP62hq5sCN91krd+68yZo8fLg1N8Pv3wN1i+DDT8O/H4C33gwH18NPL4J9L2V9adM0+be//xsffeSj+WWDJ9AbV82hvT/O49ubWde2jgsWXADAy60vl+T1O6NWxjWfMsdSrf6w33NBzQLqffW09E+zjGs6BbGeERlX070PAyfRVDT3hbPgwOfLYVnXoNdJOJEqeF96Mb6z9jt8/unPWxdTJ0BHtIO+RN+REbiWqIS+I9KBy+GixlNDwB2gwdfAgdCBUhyiTJBUOkVXrCtrZeGqRuvc1t7La3tm3zN8/fmv89m/f7bo7Qel1B9Lsml/L6ctPuzzPxEBl58TF9RR7XNpn+s4KHAtoQuPacIw4KFNVkmlnXE9vMcVwDB9pIjw9lPms2xmNbe89zTC8RT3rsv+wdod6876Ooc7uu5YwOAvW9fwptVzuPCYmXkdu90r+dLu4VnfvZ1hth7qG1FuvGpeLRv29ZT0pOBATwSHATOrByfgLm7Ivss1nLRuS6c8BLNMHC5EjaeGlJnC603SWOUZFrjGk2ke2dzKJStn4Rq4evbWk+ZhGHDXSyNPDn+/di9Lm4KcmmdQOZozl9YTTaRZv6972O2v7u0hlTZH7W893D9euIwbz1lCLJXm8S2t3HDL83T0D89Q7O/fz8zAbDbv7+e0IX21s4OzcRrOYath8jH0ivd09c5j30nSTLKifgVXHnXlqI+t8dQUVd5ml2DPDMzMXGkupFy4N96b6W+1j6M/3l/cv03/DHjP3XDsm6zJww990VqbY5pWhvW+f7Xue/99MOs46zknvANuegicHvjlm6BtZObyvl338USLVYJcyrLcYlxwTBNel4M7N1g7r9917Lto8DWUrAzN/tzPp7e7lBlXt8NNtbua+VXzC/63mg87cPU685tQnkvWjGt04N/NkIxrb6wXw9PJfN/JwChlnoGBz5fDdrn6PU5M01qpM5H64n209LfQG+/l7tfunpD32N1r9fDmKhWuJKXMuDb4BvfXN/gbMlVAR7KeWA+fefIzhVflTILOaCdpM50143rSzJOodlf//+ydd3gc5fm179neV713uVvuNtgGY3rvLYSE/NJI7yGkkJBC+NJ7IAkJkEYoCRBCB9Mxxca927Ikq9fV7mp7m++P0ay02tnVSlrZButcly7DastImnnnPc85z3kSlFVv2Mv33vweNbYa8vR53PzqzQQigaTXHk1sb3USE0nOUBhWXDVqFafUF/DqwYk5q2YwghnimkXkW/SsqMrlxf2SopCqxxUgENRh1IfjfYSziizML7Xx1C5l4joYSJ4HC3C4z8O6n75I/beeov5bT7H8B68RDRZisXVx6yXKQTJKKMsxUmIzsKXVmfD4G8N2BtkmLGNRuR13IEKrI3sV5k5ngGKbIU4QQVJcO11+gpHEXiX5dxuNGKesuMq9gUOhIeoLLRzqHQmpeuNwP0PBCOc3jIRileUYOaW+gEe2tRMbNROysXeILUcGed/Kyqwklp5UKykJbzUl9tPKwUzLqnIyfi+VSuDWSxbw2GdP4a8fPYlOl58P37s5YXZvh7cDs0qysZw0atHVqDSUmEsmpbjq1Xqs2vR9uMczVpeu5sZFN/KDU34wbhrlZOcP9vp60ag05Bny4r09EwlocgfdcZswgElrIipGCcUmmaiqNcK1f4cVH4GNv4Z/XAH3XS0FOS37IFzzN2kkz2gUL4CPPQcaAzz2WUlBG0Y4FuYnm3/C7NzZADQOHtuxFGa9htPnFvJ2h1S5X5C/gEWF2eufGgwMolfrE1IxU8Gmt2VFcR0IDFBgLEAQBMqt5dNiFQ5EAqgE1ZRGlEAKxXV4PR+tuO7qkwKZ5prXA2n6XM3DxHVMsrBpON3fO812YVnlsWgt/H3P35WDp6aIFlcLANW26vRPfA9Avh9PdZbrQGAgwe2Tb8iP5y6cyHi1/VWean6KJ5uePNaHkgTZKVRgSi5269V6zq05l+ePPB93Nty96266vd3cdsptfHv1t2l2NbOxc+NRPeax2NziQCUo7M+Ge1wB1s0poNMVSJnfMoP0mCGuWcaq2jz2dbkJRWI4g040Kg0mTWKP6YAniMevJt8qohrVo3jRohK2tjoV7cKuoCuJuHa5/Hzo7k34Q1E+tb6OT6+v57Onz2J12RLM1q4E5TITrKjOTVJc325yUGDRUV9oSXhc7n/c2Z5+0yWKIm0Zktsul59Se+KGuNhmQBRJGmsgzz6MhE1T7nGNB+sM97k29o6oVc/u6cGi17C2PnEhvWZlBW0OP4/v7Iw/ds/GFjQqgSuXV0zpeGTkmXU0lNsSAr9AUsXrCs1x6/ZEsaomjzs/sJy9XW5u/Ns7BIaTNzs9nfQ5TRTb9KyqTSy2VFgqJmw/7Pf3xzfT71YIgsAXln+BeXnjW+4nq7j2+nopNBaiElQUmSSHxEQVV3mzB8TXmynZFlVquPhXcOZ3JAW17wCs/zpc+ntQpyAutlK44KfQvhle/pGk0gJHXEdwBBx8ZOFHKDQWHhcBKecuKMEntFJoKMWut7OoYBHNruasqJ+OgINcQ25G571Nlx3iKqtLIF2r3d5u5ZEzU0AoGkKv1k/5etaqFFKF/U7pX+PIurN3eARXjXkh5Zby1AWPuOKaqKaZ9NJ5Ot0BTTJx/crKr9Dp7eStrrey/hkt7hZ0Kh1l5rKsv/fxBo1Kg1VrnXo4k38gwe2TZ8ybUVyR5pEDcQfM8YTxXFoX112MP+LnxbYXcQVd/Gv/vzin+hyWFi3l5NKTERCmzdETjYlsbnHwRmM/7jRjE9854mB+qS05mDMckMbRQXxKxti2tBlkhhnimmXMK7ESiYk09XtwhVzYdfakG/3rjf3Eogb0+kQydsFwmM8zu7uT3ncwOJgw3iASjfHpf27F7Q/z14+cxNfOm8dN583lpvPmcs6sVQwEBibcz7GsKocOp5/eUam6bzc7hhNuE3+GTAOa/vxaE+t++hI72pzjfn6XK0BpTqJKkWeWyJnDm/i7kjd74bAR0xStwqMrvLOLLLgDEfo8QURR5MX9PZw2pyA+m1fGxYvLWFxh57Yn9uHyh9lyxMH9m1r54OpqCidYMEiHCxpK2dbqpMMpFTNEUWRbm5MVE7AJK+HMecX8/JrFvNk0wCf+sQVPIMSA30GPQ8+N6+qSZseWW8snHM6kFGv/XoZNZ5uUStDj64kTVtkiNZE+UNkqLEMelyHb6ScNQYDTboKv7oMv74YzviU9lg6LroZF18CrP5OU2r4DHOrdDsCc/c8zKwqHjrwCL/8Ytv8LIsdmzubpcwtRGzowIylY8pxAOT16KhgMDI4bzCTDrrdnzSosq0uV1kqiYjQ++ztbCEQDU7YJw7DiOpZUy4rrKKvwwcEDxCIW8o2FzMqZlbrgkUpxHS5oTvdInP2O/RQaC1lfISnDE10nM0GLu4UqW9V7Kj04HbLhRBjwDyQEA+Yb8uO94CcydvfvBmBrz9asrD3ZhFywVbIKAywvXk6ZuYz79t7Hr7b8Cm/YyyeXfBKQ7ntVtqppCeASRZGvPrSda/74Jtf/5W0u/d3rSXtSkPpbN7cMcnKtQq5HJADDLpy64TyV5v7000RmoIwZ4pplyONJDnQPKaqkAK8f6kcrmIiSqKzWF1qYW2zl6V2JGw5RFHEH3QmW47tea2J7m5Pbr1yUNJ9zceFiILmJfTzIYT+yFbXN4aPD6U+wjcrQaVTML7OxPQ0hPTLg5RfPSdWvf76VPGdvNERRpNPpp2yM4po/TFwHvMqKayhomLriqhsZeD6rSPr7NfZ42NvlpscdVOwTVqsE/t8Vi3B4g3zgL2/xhfu3U2Y38rXz5k7pWMZCTiZ+ethCvr97CIc3xPIs9NBesayCn169mNcP9XHpH55BJIZBlaM497fCUsFAYAB/RDk8TAljK97vddj0k+tX7PX1xomrSWvCorVMKFxpKDSUSFyHFVdv+BjcFAUBrvyzpNa2b4Y7V3Po+W+gFkVqtz3E7IEjNIWcRF/+Efz30/DoJxJsxUcLOl0IlW4Al0vq3V9YIPXrZsMuPBgYzHh2sV1vZyg0RHSKv4PRtkg5BTzbduFQNDTlRGEYT3HNiT804Hcghu3oNSrqc+ppcbco/54MOSCok3pc5fvCdCuu+xz7mJc3j3xDPipBNeGxYZmgxdVyQtiEZUw1tCwmxuL2eRl5hjz8Ef+0BWi9GxCJRdjv2M/igsVExSgbO46trXYs4lbhFPsGlaDiY4s+xj7HPh4+9DDnVJ/DnNw58e/PyZ0zLYrrH19p4r/bO/nU+np+9/5ldLoC3Pj3EbeajBf39xKKxLhgUUnym0RGFNcck448s46m/hnFdTKYIa5ZRl2BBY1KYP8wcR07BF4URV5v7KfUmqOYHHrholI2H3EkqJ6esIeIGImT4CMDXn79/CEuXFTCJYuTR67MzZ2LWWtmS8+WCR37wjI7Rq2a5/ZIN963m6Xq5Ml1yqmwK6py2dHuVByhA3DrY3vQqVWcPb+Ix3d2prVXDPrCBCOxhERhGK24JgYJyQEo/iwQ17jiOnokTp+Hlw9Ii+j6ucrVv4ZyO9+7dCGiKNlIfnzVIsz6qam/Y1FTYGZBqY0nh4nrfW8fQadRcd5ChYVxErh2ZSV3XL8cg1EiOZc2zFP8GeLJwhNQE05ExXWimy1RFOnx9VBsGgk/KzIVZUdxPVYbNEGAlR+FL+6AtZ/nUG4ZNZYKdN/uZtZ5PyeoEmj/4lY45wew51H42yXw3LfB2XrUDlG2d3b25tHvCWLT2ai112aHuAYHM0oUBumcEREnlCI9FjExxmBgMK4uVVorAbKeLByMBqecKAwpFFf/cIvKKMXVG/YixvTotWoKjYVEYhGGQkMkQaWSZhAnKa7DVuEMelx9Yd+kiFIwGqTJ2cS8vHmoVWoKDJNM9E6DaCxKu6f9xCKuOvuUrMKuoIuoGE3scR3+7xPZLnzYeZhANMB1864jV5/Ly20vH+tDSkC/vx+73p62QHbt3Gt5+dqX+dn6n3HLybckfG927mxa3a0Z3/v+/GoT63/2Em81pT4ntrc5+dmz+7lkSRlfP38ulywp49fvW8qWI4P8akMiSX5mdxeFVr2yIy4SiPe4AtQWmGnqm1FcJ4MZ4ppl6DQq6gstccV1dGgKwOE+L12uADV5eXhD3qRUsQsXlSCK8MyeEdVVDiKSies/3zpCTBT57iULFfuNNCoNS4uWTpi46jQqrj+5isd2dHJkwMvbTQPkmLTMLVYO11lRnUsgrDxCp9Pp55WDfXzitDq+cNZsAuEY/92WmvR0Dlthy3LGKq5ShWpsj6sr6EKv1hMIqqdsFR49g7PYpseq1/Dm4QFe3N/L4go7RdbUm7UPranhyS+s461vncW62coEd6q4aLFkF37pQC+PbO3gksVlcUKfDVywqJRvXCwR06uXzld8TnyWa4YBTaFoCFfQdUIRV7veji/im1BvoSfswR/xJxDXQlNhxsQ1JsYkxXXUOiPPep6yVXiqMBfAOT/gkMHE7ELJiisHNB1yN8MpX4RzbpOUsrf/BPecDwOHj8qh7RuQgn+i/rJ4gWpRwSJ29aWfdZwJ5B7XTCAXNqcSROMMOhM26YXGQrQqbdaThYPRYFYUV41Kk6y4KoQzecMeibhqVAk5BIowF0xJcb397dv54FMfnPDfvnGwkagYZX6+tG5OtOiUCXp8PURikXhB4kTAZIPuZCiNYpNdECeyXVjub11UsIjVpavZ2qs8vuxYIdUM17HIMeRwfs35SaP25uTOQUTksHP8+8iRAS8/e+4AHYN+rv/zW3z8b5v568ZmoqMCN0ORGN94eCdFVgO3X9EQ329fuKiU959UyZ9fbWLbsEPRF4rw0v4+zl9YkpBdE0c4IIUXDqOuwEzTjFV4UpghrtOAuSVWibgO97iOxsZGaUGdW1hERIwQiCZGd88utjKryJKQLizbYnP0OQTCUf69pZ1zFxZTbEtNqFYUraDR2RifJZspPnlaHRqVwKf/uZVHt3Wwbnah8kWINPsVYMuR5M+QA4UuWFTK4oocFpTaeGx7Z9LzZHS5kme4AtiMGjQqIamfQB4P5AtFsqK4alQaBvwDCILA+0+u4und3Ww5MsjpGY4Tmk5ct6qS2gIzH7l3M75QlP9bm/3K+4BfqjimIpqy4pqpiiNvDjK5Cb1XMNpynilGz3CVUWQsyjicyRv2EhNjilZhfzhzW/d0wRv20uHpiBPWOnsdAsJIv+IpX4DPbYIbX5Qq0nedDv/+CGz43sjXnkfjQU/ZQpe3C5PGRL6xIL4mLypYxEBgYEq9oYFIAH/En7lVePj+MBV1Sb525U2cWqWmyJT5OZQpsqW4alVaBcXVCWp9giLhi3ghZpCIq24c4mrKT5rjGieuGfS4buvdRou7ZcKzfA+7pA3y7Bzp/J4O4ipbvuXi4YmAXEMu/f7+SReRZFU1ocdVVlz9J67iurt/NxathSpbFZW2Snp9vVkPcZsK5EDHyWJOjmQbHs8uLIoi3/2f5Ah87sun8aE1NRzu8/K9x/cmjDm869XD7O8e4rbLG7CNCVv61oXzKbEZuPk/O+NjE/3hqLJNGIbH4Yysn7WFZvqGggylcSLOQBnHlLgKgnC+IAgHBEFoFAThG8fyWLKJuSVWOpx+nIHk2asbG/upzDNSYZcq8kp9aBcuKmVTs4O+IckeG58Ha8jh6d1dOH1hPnByevKyongFwIQrakU2A+8/qYq9XW5Om1PI/7uiIeVzS+1GynOMisT1+b091BWa49bbtfX57OpwEUoxU09OUi4do7gKgkCuWadIXG06OzGRKY/DUQkqik3FdPukDes3L5jH186bi1mnjveYHkvkW/Tcf+Nq6grNrK7LY3FFTtY/Y7w0vzxDHiaNKWMVR940n0iKq6wKTcRyKBeW8owjRKfQVEivvzejTZtMkhV7XCPHvporB2XIG3uT1kSZpYwmZ1PiE0sWwUeegXkXQ+ub8OYd0tcbv4N/fxj+fhnsfAic2VER5f7r5VU58T59OaBpZ//OSb+v/PfMNJwpriRmQ10atUnP0efE7xvZwrQrrqPUVpCIqxg1oNeoR4hrqt+TguJqzNAqPBQaiq9rTzQ9kdkPMQz57x1XuyfglsgUcrGwwnLiENdqWzWesGfStl6l+5l8fZzIVuF9jn0syF+ASlBRZi4jJsayfr5OBX3+zBTXVCi3lmPUGMdNrn9ubw8vH+jjS2fPpq7QwvcuXciLX13PksocfvncQfyhKI29Hn77QiMXLS7lnAXFSe9hNWi57fIGDvV6+PWGg/zoqX3UFZgVM2GA4XE4oxVXaW88E9A0cRwz4ioIghq4A7gAWAC8XxCEzAePHseYV2IFIUwgGkjocRVFka2tg6yqycOsk+x8SurMxYtLiYnwjzdbgJGNsEVj4y+vNVNbYGZNir5TGQ0FDehUOrb2TNwKcvP5c/nTDSv484dWJkd6j8FyhRE67kCYt5oGEi72JZU5hCIxDnQrq1GdzgBatUCBOTm1Mt+sSw5nCjixaqXfrXmKxBWg2FRMj1dSiQVB4LNnzGLn986Lh20da5TYDTz3pdP460dOmpb37/P3YdVaMWiUFRVBEKi2VXPEnT5kS0Z846Awj+29Clk9m0hAk3z9jx5nU2AsIBKLZPQ+8nNGE1fjsGp1PISQyBsIWXEFSb1XVDUL58AVf4Cv7ofv9Elf3+6Di34BXTvgkRvh14vgn1dD22blD/Q54J17oGdv2uPqD0iV/aVVOTT3e3H6QszJnYNOpWNX3+T7XB1ByWmQsVU4G4prIFFxBYm4ugJTH7MzGsFoMCupwikV11H9raIo4o/KPa6ZKK4FST2u5gytwgccBwBJLX225VlC0cyTrp1BJxpBg0UrbUKLTcW4Q24CkcA4r8wc7UPtqAU1Jebs5Bq8G1BrqwWg2dU8qdePdSHAKKvw8CzXAU+Q1w71JQXsvJfR4emIW85LLVJRvtOT2gl3NCGKoqS4TmHPoBJUzM6dnVZx9Yei/ODxvcwrsfLhtTXxxwVB4JYL59PtDnDTv3fwlYe2Y9Sp+d4lC1O+11nzizl3QTF3vnyYnqEgv3rfUjRqBVoVi0I0lGgVLpxJFp4sjqXiehLQKIpikyiKIeAB4LJjeDxZw9wSK4JaUhBHE9c2h59+T4jlVblYtdJGVUlxnVNs5dIlZfzx1SbaHL64VfiJbW72dLr50tmzU9p3ZejUOhYXLp5wnytIoRbnLSxBPc5nAKyoyqHTFYj3qAK8tL+XcFTk3FHEdWllDgDb252K79Pl8lNiNyj+XHkpFFez1hY/3qmi2FyctJnO5Oc/mtCoVUljebKFTG4YEyGu8XRAw4lDXCejuA6FhxVT7QjxlDfBmQT2yArUcdnjitQDaNJIKquM0e6GcaFSwaqPw9cOw6c2SrNku3bA3efAI5+ADd+Xvp69Be5/P/xqITzxZbhrvaTWplCt+/395BvzWTrsXtjR7kKr1jIvf96UApr6fcm9dekwmXNmLJRs/jmGnPh9I1sIRoLoNdkZhzOe4hqMBomJ0RGrcCY9rv5BiI68rzHDcThyUNcXln0Bd8jN6x2vZ/yzOINObHpbvPet0CSpRdm0abcPtVNqLkWjym7w3/GMGnsNII0BmgwGAgPoVLr4PgukPZFVa6XL08cn/v4OK2/fwA13b+LC376WdjrCewXhaBhHwBHPU5BnAh8vxNUdchOOhafcXjQ7Z3baHtc/vHKYDqefH1zWkEQyT6rN44Orq3hmTzc721185+IF4443/O6lCym06vn6+XNZMrzPTUJkOFx0FHGtzjehEqTcmxlMDMdyJSwHRvu+2oGTj9GxZBXlOUaKc6J4SSSu8piZ5VW5+FTSjS3V5vSbF87j+b09fPIfW9AWHAAE7nixnfMWFnPpksyGkC8rWsa9u+/FH/Fj1BjHf8EkcMosabP01K4uPr6ujmhM5E+vNFGRa2Rp5YjqUJFrJN+sY0ebkxtWJ9ucu5yBpP5WGXlmHXs6EzcsrqCLuXZpMzNVqzBAibmEDUc2EBNjqIQTr/U7k96Sals1zx15jnA0jFadXokf8A8gICRYYN/rmIriatFZ4o/JxDOTcTZKiq1BbUBAOC4U105PJxXWioRrqsRcQp+vj2gsmvlcSrUGShqkr7Wfgxdvh61/g+iwcqdSQ24NNFwFSz8Ab/5eSiru3gWX/i4+hkBGv7+f1aWrWVRhRxBge6uT9XMKWVywmIcPPUwkFsmcKMRicOhZ6DtAZ0Ra1+We8PEwrpKYAQYCA2hV2oRNeo5+GojrdCuu1pG2DPm+KIUzqTPrcQXwO8Ai9Yvr1CrUKgFfKL1VeJ9jHwXGAs6vPZ9vb/w2jc5Gzqw6M6OfZezIO7lXvdffS6UtdZiSKIrcvftujBojp5Wflva5bUNtJ1QwE0hrhEFtmJLimm/MTwqvtOlyeXz3Qdyty/ns6bOYXWzhp88c4CP3bmLTLWejVVLL3iOQi8nyORpXXL3HB3Edr10pU9TYanAEHCmnevz7nTbOmlfESbXKe5MfXr6Ib188l1+981v2hN5hz5vjf+ZFZ0CXsJHvp3puJAj5udD/Jrw5UqTMrerk/sMCz/dmL2wzUxjVJh657sdH/XOzgeO+hCcIwieATwBUVSXPlzweIQgCK2r1vOoBg2pkM7G1dRCzTs3cEiuNTulxT0iZuJbajXz/0oX85oVD+F19CAYzDWU5/PDyRYpJwkpYUriEiBhhT/8eVpasnPoPpoDZxVaWVeVw/6ZWPnZqLQ9vaWdvl5vfvX9ZgmIpCAJLKnPYkaKy2TLgTZnKm2/WMeAZGYcTE2O4Qi4MamkzY9ZngbiaSgjHwtJYiQzVkvcS+v39NOSn7mcGibjGxBjtnnZq7bVpn9vn7yPXkItWlZ7gvpcwmX5FeSM+mrjKimsmxFVWVc0ac/wxQRAwaU3HZo7rGPT4ehKCp0BSXKNilH5/P8Xm5N6hcaG3wgU/lr5SoWo1vPYLePE2CPvg2n9IY3qQCNhQaIh8Qz5Wg5ZZhRZ2DDtBGgoa+Oe+f3LYeZi5eRnOZH71p/DyjwDozM1Bb7eRHwxABrVCnVqHUWOcsuI6dpNu19vxhD2EY+GsXYPZJK6KimvRSKK5fO6KUQMGrQqDxoBOpUt9bcnE1dsfJ66CIGDSqfEGx1dc5+XNQ6/WY9FaJpQ6K4cEyigyDhPXcfoGW9wt/GbrbwD4nfZ3PH/18wnFp9Fo97RzTvU5GR/TewEqQUW1rZoWV8ukXj/gH0jo+QbodgXodmiJ4ub+G1ezcrgX0aBV88l/bGFzs4O1s967DqGxQYB6tZ4CYwFdnq50L0vAD9/6IUsKl3BJ/SVZPz75uhv7d5so5LFRR9xHWFy4OOF7uzvcdLkCfPXc9Gv7Yech/nXgb9h0tqz09ROLgckE3iPQNrI2qC0RAuEYHcE0r50mqDGP/6TjFMeSuHYAo8uIFcOPJUAUxbuAuwBWrlyZ3WjJacSCCi2v7ofDPTHWD/Ptra2DLKnMQa0S4qpKOjvgtasquXZVJV95+QkanYU8cvkpEzoG+aLd0bdj2ogrwPtXVXHzwzt5YHMbv3juIMurcrhYYb7skoocXjrQy1AgnNA7OxQI0zsUpL5I+ULKM+txByKEozG0ahVDoSFiYgydIG3wjdrsWIUBun3dJyxxzcQqDNINYTziKlsxTyTIqtBE+hU9IQ9GjTGBXMj976mKWqMhq6pGbSJLMmlM+CPHPlW4x9fDgvzE6AK5V6/H1zM54oq0yXlw/4OUW8s5peyU5HNNEOC0m0ClgQ3fhR0PwNL3S68d7nGTK/tLK3N4Yb8UhrW4QFozd/bvzIy4RoKw+S8w62y46i90PvcpSvt2Irx4G1z5p4x+lsnM/x0Nd9CdFAIoh0NlcyRVtoir8hxXV0KP61jFFaTCUFqrMCjMclXjT9PjGowGOew8zPqK9YBk8ZbPj0zgDDoTQpOKzJkRV7mv9ovLv8hvtv6GTV2bOKv6rKTnDYWGpM84gRKFZdTaa9ndvxuQ/k53br+TWnstl8+6fNzXDgYHEwiQwxvig3e/TURvoqJoKE5aAdbNLkCnUfH8vp73NHHt8UkZHqMLiWXmsowVV3fIzYMHHuQ/B/9DniGPU8oz2496Q15+u6GVXR0ubjpvLsuVZpwC2zskA+bn/nGQAn2Qq1ZUcP1JVRN21I22mY8lrs/t7UYlwJnz0k+LkIPtfn/W71lWtGxCn6+IgcPwu+Vwxa2w5Lqpv98JjmPpi9gMzBYEoVYQBB1wHfC/Y3g8WUVRjlRR3tIklVJ8oQj7uobiF61cXc1kczrWjpQpcg25VNuq2d63fcKvnQguXlKKRa/hm4/sAsSUqvCSSjuiCLs6Ejdp8hDm+kJL0msA8ixSxWtwuM9VtsDJxHWq43BgZDM9lVEY71Z4w178EX9GVmEgoz7Xfn//CTUKB6QNuVlrnpDiOhQaSlJa4oprBqnAsuIqJwnLMGvNx9wqHIqGEnqqZMSLRFO41h459Ah37riTW16/he9s/E7qJ679PFSthadvBpdUFx1rSVtSmYPDG6LV4aPCWkGOPie+YR4Xex4Fbx+s+SwYc+lUS2md7HxQsilnALvePiWrsCfsiZ8zMuT7hTwDPBuYNsU1FoWgK3GGa2j43B8OZwKJ4KcNZwKFkTiatONwGp3SHNZ5efMAKcBnIqmzrkDivdmqtWJQG8Ylrvsc+9CoNFw/73pMGhNvdL6h+Dx5bvaJlCgso8ZeQ6e3kw5PBx9++sPcs/sevvvGd3m7623F5zu8obgt3BlwxgPSPMEIH7l3E60OH2fMrscTTQyTNOk0nDqrgA37eqY8w/l4hnxOjl6PSy2lCYqrNxhJmGM6GgcdUuCRSWviplduymht2TuwlzX3r+Vvh7/Pjs4WrrzzDR7c3Jr0vNcP9fPrl6Qg0bW1Neg0Km57Yi8/eWZ/xj8fQKfTT8CXg1rQKKr1z+/tYVVNHnnm9Crq6BGUWYFCj+sMJo9jRlxFUYwAnwOeBfYBD4miuOdYHU+24RkOXXn9oI8ed4BHtnYQjYnx2acmrWn4eeMT18Hg4KQvoCWFS9jZt3NaF2STTsMXzprFRYtLeeqL61hQZlN8XjygaYxd+HCf9DuoL1RWXPOHF5mBMcRVjXX487NjFQbiycInEuSN/HhE0663k6vPzSgwo8fbc8IRV5AKUhOZ4zoUGkpIBIZRPa6h8YmrrKqOTYM2aozHPJxJ7qkaq6rGrzXf5K+1zd2bqbfXc37N+XH1ShEqNVx+p9QL+/TNQDJxXVsvKTP7Nz6OsP0+GnLnsrMvw5E4b/8JCuZA3RmA1C9WVnEyGOzSDNoMYNfbp6S4KhLXYfUym32u09bjKqcfKyquUjgTDBPXdONwIHkkjlaNP02Pa6tb2kTX2esAibhmahUWRTHJKiwIQkYzdPc79jMrZxYmrYmTSk9iY+dGxXv0iTjDVUatrZaYGOPTGz7NYddhfrTuR9TYarj51ZsT/kZ7Ol1c8rvXWX7b8yz9/vP83z2bcAQG43PvP/H3d9jd6eaO65ezoKgMV9CVpPifs6CYNoefgz3j78ferej19aJT6RL6PsssZXR5u6QWoEEf63/2Mh/962ZiCuT1wKC0zt66+lY8YQ87+naM+5m/2vhfYqKI3naI+cseYU1dPj98Yh+97pHU7aY+D5/65xbsliAqVPzy6rX897OncN7CYp7d053x3vXPrzax9scvcsFv3kAM59E8hri2DvjY3z2kONpmLOT1OHvEddj9pJ2erJkTDce0E10UxadEUZwjimK9KIq3H8tjyTZcQRdqQUM4rOHC37zGdx7bzamzCjh1lrSZ16q0GDXGzBTXwOQUV5CIqyPgiN8ApwufOK2eO65fTpE1dUUpx6SjJt+U1Ofa1OdFrRKoyktlFZaIq5wsLC8qalEi/yb91K3Ccj9mxmmn7yHIm6xMrL2ZJAv7wj56/b1U2d4dPenZhEVrmVBvqZLimkkbgQxf2IdRY0wKFDseelzlItDYHle73o5BbZi04hqOhdnWu42VJSuZnz+fXn9veuKXVwunfx32PwEHnqY/kJj8W5er5dfW+zhv6yfhsc+yeN/zHHY2jv/769oJnVul1GNBCsNyBByU2Wth3VehcQM0vZL69ZEQ7H4Em9o4NcU15Inby2XEFdcsEddoLEokFpmeVGFZuRmtuMZ7XPXohgNz0lqFjcP2wzGKq1mfvsdVvi/Kqdf5hvyMias/4icUCyUFwBSaCtMWZURRjPfVApxSdgodng5ah5KVKPn4TrRwJhixfDa7mvnKiq9wcd3FfH/t93EEHGzq2hR/3s+ePUD7oI+bzp3D/62tZntHH4Gon73tUW78+zu8cXiAn129mHMWFMev+bF28LOGraMv7H/vFq7lvIHRbrgycxnhWJh2lxQE6vKHeOVgH3e91pT0+v2O/eQZ8jit4jQEBPY60o8c+8+Wdl5vfwurUMVHG/6PRudBvn/5PILRGF95aAf3vN7MPa8386l/bkGrFjh1noFcQ278XnbW/GK6XAH2do2/Nv7jzRZuf2of5y8s4SvnzCHkz2NXb+Is1z++ehitWuCCRcltbGPhDDoREJKKypNGeJioZ2H9nMExJq7vZUiVWDv/+dQpGHVqTptdyF/+byU6zciv3Kw1j7s5VarqTgRLCpcAsLV34vNcpwNSQFPiJvNwn4eqPFPC72Y0xiqu8tB3ISZt1kxZGBGjElTSmI7j0Cr8VtdbrL1/LUv/vpRbXr8l6+8vb+Qz6YWrtlVzxJWeuLYNSb0qJypxlUfcZAJ3yJ1EXGXbb6bhTGNtwvJ7HGvFVcmaBpIqVWwunrTium9gH/6In5UlK5mVMwsg7fgDANZ8DooWwH8/zUDXNmA4BCTggn9exeXhJ7knegFDH9pAQ+FiRGDvC2ksyADb/wVqHSy6BhixPpdZyuCkT4C9Ep6/VQrmGIve/fCXM+E/H8HeuQP3FBRXb9ib2iqcJeIajEpWt2wprlExSkwc/r34ndK/CoqrTmWKb7StOmtq4qrWSq8fq7iOYxXu8HSQZ8iLO6DyjHkMBgaTw6MUkEqVKTIVxd0GSujz9+EIOBKIK6BoFz4ydIQcfU7K4Kb3MmpsNagFNSeVnMS1c68FYEH+AtSCOj4fusPp55WDfdywuprPnTmbWy5awF8/KvXUv37Az1tNA/zgsoVcuVxSrGX78NjroshmYFaRhXdaEm3E7yX0+nqTiohywebXr25ib5ebu25YyUWLSvn5sweS5osecBxgbu5cTFoTNfYa9g6kJq4uf5jvPb4dramNK+avpy6nlqgYRaN38LVz5/J6Yz8/eGIvP3hiL60OH7+5bhmBmCthCsGZ84oQBHhhX3rbfZvDx21P7OPMeUX87vplfO6MWVjVZfT6O4jGpGu/sXeIBze38YGTqynPGV/1dAacWHXWzFPvx4M813mapnucaJghrtMEd0gKzFhQZuOVr53BXz+yKmkGp0VrGZe4BqIBxapuppidO5sCYwGvtKep/GeAhw48xCef/2R6W14GWFKRQ7c7QLdrtFXEm9ImDKMU1+FkYfmmI0al12RjHA5Ifa7Hm1U4HA1z+1u3Y9fZWV68nKebn866kibPgczE2ltjr6HX35u2f1JWDqqtyWOP3usw68wZWXxlKCmuapUao8aYMXFVGnV1PPS4ysRUKYCpxFQy6SLROz3vALCyeIS4Njob079IrYXr/gXmQvp3PYBdbUDr7Yd7L4TWN2k57Zf8IHwDG1ylLLriXgB27XsIsW0TT+/q4lP/2MLn79/GM7uH+8EiIdj1EMy9EEzSZkvuRyy3lIPWAGfcAl3bYecDicfS9DL85WwY6oYVH8E22IZrAkm2Y+EJe+IqvQz5fnE8Eld5zFCcHKZRXHWqkXM7rVUYJLvw2B7XcazC7Z72BBtuniEPETGj31uqPrhiUzG9vt6UFkd5bqxMXCttlZRbytncvTnpuc2u5nGD8N6rMGlN3HXOXfxi/S/iKpxOraPaVk3joHS9P7RZKpJes3JEkTYapXP119ecwv7bLuBDa2ri35NDy+TwndFYUZXL1tZBRZvsewG9vt6kImKpWVIfn9m3j0uXlHHGvCK+c/ECIjGRJ3aMhDaFY2EanY3xc3ZB/oK0xPWfbx3BrzqMKERYU7Y6fg43u5q58bQ6dn3vXHbcKn1tv/VcTptTiCPgSAjUKrDopeC8fen3ZD9+ej9qlcD/u2IRWrUKlUrgjPqFiEKYv23ajjcY4dbH9mDSqvn8mbMy+l1NNlcmJSIzims2MUNcpwmjZ0ipVYJiWFEmxFW+UcujNiYKlaBifcV6NnZsJBQNTeo9AJ5qfoo3Ot/guieu440O5SCJTCAPaJbHT0RjIs0D3pTBTCBZjAUh0SqsFtREwjrUKiHeAzVVTEUFmi7cv/9+WtwtfPPkb/KZJZ8hHAvzesfrWf2Mfn8/akGdUXGkxlYDQLM79Xw92Up8IiquVq01I4uvjKHwUML8TRmZWo79YX9cLRoNk/bYK649vh6MGqPizzeVa+2d7neosdVQYCyg1FyKSWMaX3EFyTL8secZsBZR4HfDnathsAU+8G+qTv8oRVY9T+7sJsdUQKWlnJ1mG4f/8Xk+fd8Wtnf08orr/3HzK9/AGwxLc1t9A9LM2GF0eqSNnqxisPhaqDwZnv46OIdtoDsfgn9eDTmV8IlX4OJfYS9aQFCMEnCOH3o2FqFoiHAsnFT8MGqMGNSGrIUzZVtxhVHEVUlxDXkQUCdYk206G0PhobiKkgRTQXKqcAZW4dEzd+WNcyZ2YZm4JlmFjYUEo8GU6rBMXOfmjqRWNxQ0KBKBZldzvP/2RMRJpSfF+7Vl1OfU0+hsJBaTZnKeOquAyryRNVB2ZJVaChJG8sEoxVXhulhRk4vTF6ap/9i2WEwHRFFUVFxl4hpROeKkrsRuYEV1Lk/tHiksNruaCcfC8aT1+Xnz6fX1xvMCRiMQjnLvxmZqK7tQC2qWFy+P7xvkwCSrQYvdJH3Jgs6Af4A8Q+Js1bPmFbGj3ZXQEzsaW1sHeXJXF59cX0eJfaRN7bKFktPw9udfZf3PXubNpgG+eeF88i2ZrV9TcTkqQiauMz2uWcEMcZ0mOIPOccmmRWcZt8dVHq0xFa/9mVVn4ov4UqbxjQdRFGl0NnJu9bmYdWaeO/LcpI9lYZkNjUqI97l2DPoJRWLUpVFc1SqBXJMuIZzJrrfjD8cwadUZz7UdDyWmEnp8PRnZxI4GorEof9n1F9aWrWVd+TqWFi0lV5/Li60vZvVzHAEHeYa8pD5JJdTlSJuodEShbaiNfEN+kgp0IiAT+78MURQVFdeJvE9aq/CxVly9PRSbihWvz2JTMX2+vtQkJAVEUWR773ZWFK8AJNuxvJHNCMYc+gtnUWAulRS+jzwF9WeiUglct6qSDft6eONwP4sKl/CG3sgmfTvfXPIGy1Y8jmjcj2jZyjc33Amb7wZLCdSfGX/rTm8nWpV2xHKvUsOVd4Eowr/eB49+Ch65UZox+5GnwV4OgoBtzsUAuLf+dUK/Cxix1CpdazmGnONacY0H5Cgorp6wBw1GDJoRN418D0x5XZgLwDs2VViNP4VVOBKL0O3tTkjslTfOsgslHeR7c5JVeJyROAccB6iwVCTMbl6Yv5AOT0ecdIFErhwBxwmruKbC7JzZtA21saurj05XgMuWlid8P66EjyG8MPK3UipMrKiWSO2WI5N3PxyvcIfcBKPBJOJKTI8Y01JfIjKraOQ+dEFDCfu63LQMk3jZaScXW+QRZ/sG9iV91v2bWun3hDDbW1hYsBCz1oxFZ6HQWEizK3XB2xFwJOVsnLdQCvJ7do+yO+c/W9ox69R84rTE4s6cvHoATp0fI8+s5f4bV3P9yZkX0uU9ZtYw0+OaVcwQ12mCK+jCrkt/4meiqsQV1ykQ15NLT8aoMfJS20uTen2/vx9X0MXy4uU05Dewqz+zMQ9KMGjVzC+1xRXXkUTh1IorQIFFR/8oq3COPgdfMJo1mzBIc28jsQh/3fPXrL3nVLCrfxeDwUGumH0FgiCgUWk4reI0Xmt/LXkO4hTg8DuSKp2pUGWtQqPSpCWuR9xHTki1FSYWzuSL+IiJMcVrO9P3SWUVllOFj+V4B6UKv4wScwlRMapYsR/vPYfCQ3HLGjAx4goMBBzkV5wMX9wBpUvij3/mjFlU5Zn49qO7WWS5HF+gkB8V5PH70OO83vka31n9HSyRJbzcdw/721+Hkz8J6pFguE5PJ6Xm0sQCUG4NXPEHqeK+93+w+Dr44MMJJM2WL22yXLv/I42GmQDkwufYHleQNunHI3GVFdf4GqaguHrDXtQYE9w0ciE4pV3YlKcwx1UTH5EyFt3ebqJiNNEqPNxjl4ni6hpOQx5LkIqM6Ylrk6uJ+pz6hMcaChoAElRXOb19hrgmYlbuLEREXmiU9iErqxPngsp/O9kWPBrpLPR1BWZyTFq2HMmsz9UfinKwJ/M8g2OJ+AxXc+J6/PLBPsSIhYqCxHXn/AaJMD49rLoech5Cq9LGA7Pk9XesS8AdCPO7FxtZXZdHT6CFBXkjM7xr7DUpJxL4I358EV/SPmR2sZVZRRae3NWV9JpYTGTD3h7Wzy3EpEsM6Mw35GPVWZlV7uO5L69ndd3EZspPn1V4RnHNBmaI6zRB7nFNh0xUFdluNJXqj16t59TyU3mp7aVJbWTlIITZObNZWLCQw87D8TEck8Gyqhy2HnHi8od5+UAvWrXA7OL04ROFVj39HklxldPxfOEo5iwkCss4o/IMzqs5jzu23cF9++7jyaYnxx1rMJ14reM1VIKKNaVrRo6x6gyGwkNs6dmStc8ZCAxklCgMklpSY6tJr7i626iynqDEVWfBH/FnVFiQx+akUlwzIq5hn6JV2Kw1ExNjccJxLNDj60nqqZIRn5s8wRRveeMjb6AAZuXMwhFwJKhV6dDv71cMIjNo1dx2eQNN/V6+/ZCDWMeXuHvW57m7q4dH6m/g2rnX8vVVt2KOwq2FBUSX/1/C6zs9nSM24dGYfwl8YRvc0glX/imp6i4XON3eHimJeAJIp7ja9fbsEdfhOYRjxy5NBoo9rmp9go3OE/YgiIaEDalc4Ek7y9U3ICnc8kM6NYFwTHE2ZUJP8jAmZRUeU6CWizVKxDUai9Lqbk0io/Pz5gMkzA9ucknJrjPENRFyX/s7nXvJNWmpzk9c/9IlwmpUGmw6m+JaIQgCK6py2XJkEG/Ym3bt/P7je1jyg+c491evJvRgBqPB4y4nA1IH5T29qxu1aAV14j60ItfEkgp7vKe/y9NFqbk0fu1adVaqbdXscyQqrn98+TAOb4jPnFWEN+xNWKdrbDU0u5oV96Dy9Ta6x1XGhYtK2dTsoG8o8e+xo91J71BQcbyNIAjMypnFYVcGLSQKyLriOtPjmlXMENdpQDAaxB/xj3viW3XWca3C8k16qrHcp5SdQr+/P21vYirIQQizcmfRkN9AVIzG+3Qmg/etqsQfjnLXq4f5z5Z2Lllcht2oTfuaQos+vnB1e7opMZfgD0UwZiFRWIYgCHxn9XcotZTy400/5huvfYOz/n0WP3r7R1n7jIngtfbXWFK4JOE8WlO6BrWgThgHMFXIVuFMMStnVkriKo/CqbadeMFMMKJ8ZWLTla/tqViF/RG/slV4mMweqz7XmBijz9eXUnGVH59oYUjukZJ7poDMA5qQ/i7+iD9loWb9nEI2fGU999+4mhe+up6T1t7ISQVLmP3mXfDMt7j04E/4tqOPfTot97U8GX+dP+KPz+acKOTr22Wyw65/T+i1cnFDSXHN1edOaT7saMibeJ1aN+X30qoVFFdjDoyylHvDXhANCY4a+R4oW3STYC6AWGRkLiwjM76V7MIycR2tuNp0NjSCJiOrsDPoxKQxxX8eGYUmKeROibh2ejsJxUIJ5y9IBa8aWw17BkZG2Te7mtGpdJSZFYohJzAqrZXoVDqaXE0sq8pNakUYDAxi19tTJsLmGfJSFnSWV+dyuM/D+5/4AN95XTlVvLF3iHs3tnDm3CKq80387NkD8UCnn23+GZc/dnlGYw6nG1uOOHh8OGBJXmdHr8eBcJSXDvRSZC7AEUg+389vKGVHu4v2QR+d3s54P6yMent9fD0GKYPkno3NXLa0DKNZIqK1tpGiS629FnfIrRiMJY8nUlqXL1xUQkxMtgs/v7cHtUrgjLnK95g6ex1NzuSxPuMhFA3hi/iyq7iGZ+a4ZhMzxHUaIF+EchBAKsiqSnwsgALkjcdkw5lkLC9eDsDWnomPxWl0NpJnyCPPkBe3NI2uDE8UC8vsrJtdwB0vHcYbivJ/a2vGfU2hVSKuoWiIPn8fpeZSfKFofGOSLdj1dh697FGeueoZHrr4Ic6uPpsHDjxw1JXXfn8/+xz7WFe+LuFxk9bEvLx5WR1vNFHiWpdTR4enQ1F1l0fhVNpOvLmDQLxvTVZT0yGd4mrRWTJKJ/ZHUoQzTWCkznTAEXAQESOKicIwkmCdyk6ZCi3uFowaY4JyIKv7MhFJh4HhDZpSZV/GrCILa+rzKbUbJTJ14U/BXARb7kXV8jolmjVofLP4484/xgPvNndvJhQLJV2vmSBOyEoaoPGFCdmF5Q3y2DmuIK1lSpvEySAQlRQDgzoLiqug0OM6xm7rCXkgqk9Y3+NW4XSKKyQkCxuHFVslu3D7UDtqQZ1wLgmCQJ4hLzOrcAo7oV6tJ0efozgSR97oK6moDQUNScS12l6dvZEc7xFoVBqqbbW4om0sGw57HI3xgnVy9Dkpr4tllTmojUdodh/mxbYXFQuQD2xqQ6MS+OEVDXz13Lns7x7ifzs6cQVdPNb4GJ6wJ6MckF5fL081PTVhF5wr6OKhAw+lJMexmMhtT+zlqj+8yefv38b9m1rj697o+/wrB/vwhaLMyitRLNRcMGwXfmZ3N92ebkoticS1ylZF21BbfP96/6ZWAuEYnz1jVryXdaziCiSQXRlKxydjbrGVukIzT+wcSTkWRZHn9vZwUk0eOSblYlp9Tj2DwcGURajtvdt5cP+DPHroUcLREYdUqrTwKSESBARpfNoMpowZ4joNyGRzBNKGVURMq864Q24EBMWK+kRQY6shz5A3aeI6O2c2IFWTi0xFUyKuAJ88TerxWVqZE08aTodCqx5/OMoRZzciIiXmEryhKKYsWoVl6NV6yi3lzM+fz+eWfY6YGOPp5qez/jnpsLFjIwCnlp+a9L3lxcvZ1bdrSinRMmQFaqKKq4gYt7KNxok8CgdGlK9MCKNMXJXcFGatGW8kM6uwUo9rXHE9RgFN8Z6qFIprriEXjaBJO+9SCc3uZmpsNQkqi9y3lYlFL97/Nk5RMQFly+Azb8AtXXDTAZzn34m7fx1DoaF4wvfrHa9j1BhZUbJiAj+NBFlxdRfUgd8BHZm3AciqvFJyc64hF3fQPeEALCUEhq1uSufaRCErlCOpwoMJPb8gXT+xscRVN16P6/D9dtQsV3nGtz+U/Dto97RTYi6J2x9l5BkzI67p7ISFpkLF1GylDb2MhfkL6fX1xs/jJlfTCZ0onA52dTkqXS/LqpKvY2fAmfb6zjHkpGwraKiwo83ZDAgEo8GkBP9gJMoj2zo4Z0ExBRY9Fy8qZW6xlXvfaOE/B/9DIBog35DPfxv/m/b4Oz2dfOjpD/H1177Oax2vjfvzxmIiD25u5eq//5EzHzyX2966jX8fVHZnvLC/l7tfb+b9J1Wxfk4h3/7vbvZ0d2HUGBOu38d3dGI3aplXVM5gcDBpnagpMLOg1MZTu9vjYsFoVForCcVC9Pp6CUdj/OPNI5w6q4A5xVZa3C0Y1IZ4SwiQMBJnLOTrTWkfIggCVywt560mB20O6X727J4eGns9XLo0tRtB7iNX2qccGjzEDU/fwA/f/iG3vnErVz9+dTyAKl2416QR8YPGkOAqmcHkMUNcpwFxv/44fYNyX1I6S6A76Mamt2WU+JoOgiCwvGj5hJW6mBij0dnIrNwRC1xDfmJleDI4ZVY+n1xfxy0Xzc/o+YVWqTfgwICk6JWYJKuwKYtWYSXU2euYnzefJ5ufHP/JWcSm7k3k6nMTQmhkrChaQSgWmvLfANLfMFIhfkNQsOEcdh5GQDhhw5nka3qqiqtZK82DTVeNj8aiBKIBRauwWSMdx1R60acCefNdYipR/L5KUJFvzJ+UVXiszVKv1pOrz81ovI68YR2vqJgOp8wqwBCei06wxgtar3e8zqqSVZMKLzJrzagEFS5rMQgqOJR5artcIFFMFdbnICKmVignAPk8ykqP61jF1e9MVlzDnmHiOoEeV/Pw33Q4oGm/Yz+eWDuA4kicDk9Hgk1YRr4hP7NU4TQBLkWmIsVzu8Xdgl1vVwwOWlu2FgGB+/bdRzAapMPTMdPfmgLhYA6C1k1DeXJBfzA4mFYty9XnphwTpVaH0Nl3Ucgp5Opz2dCa2HO+YW8vDm+I606S7m8qlcC62QXs7xrkgf0PcHLJyXxo4YfY1rtNUVkEqWDziec/gTvopshUxB+2/2Fc1fXxnZ18/eFd7A88TCBgochYnHIv9+i2dvLNOn5w2ULu+MByqvNNvHK4Bbtu5HfS7QrwzO5urlpeQaEpn5gYU7RPX9BQwrbOFkTEJOIq3+Nb3a08vbubbneAj5xSA0jrdJWtKmHfKvfIyq6s0RhvH3LligoEAR7e2k4gHOX2p/Yyt9jKNSuSr18Z9XZpn6LU1vRU81OoBTX/u/x//P7M3zMUGuIbr32DSCwSdzlmXXHVTn3tnIGEGeI6DZBveuMRV9lWmK4fwh1yT7m/VcayomV0eDro9mYeiNLp6cQf8ccVV4CFBQs54j4yJRuiIAh884L5rKrJjDAVWqSLvskhbURKLCXTYhVWwsV1F7N3YK9i5W66sL13O0uLliqOEllWvAwgKwFNmRZZRqPSWolGpVHsKdzRt4P6nPoTchQOjJDQTK6N8XpcI2IkbUCIbN9MNccVjp3iKluAUymu8vcmorgGo0E6PZ2KalWmc2EnpbiOgUGr5ox5pYTdi3ip7WX2O/bTNtSm6I7IBCpBhU1nwx0LSXNfJ0Bc5aLn6NEqMtIlqE4U2bQKJymuAWeS4uoJeYhEdAnru1FjRKPSjG8V9vYTjUX57Auf5YEjPwHAH062Cre6W6m0Jrc0ZGoVTmdJLTIWKdrgm13JjgEZdTl1XFJ/Cfftu49vv/5tYmKMJYVLkp43A3B7LAhClBDJ50JGimtwUJEsvtj6IqIQwtmznDMqz+DV9lcTnE2vHOwlx6Tl1Fkj4W5zS6yE1T10+7q5bNZlXFx3MSpBxRNNTyh+/u7+3RxxH+Fbq7/FZ5Z8ht0Du9nYuTHtz7thXy/51hgqfQ9h92Ly1Q1s792e9DO4/GE27OvlkiVlaNUqLHoNP7y8AX/ETSg0orb+7c0WYqLIR06pid/7BxT6XC9YVIpK6wRItgoPt2gcGmzhJ0/vZ1aRJd5v2uJOLjCqVWpKzaWKLR0D/gHMWnPKwlh5jpFT6gv4z5Z2vv7wTtocfm69ZAEadWoKU2QqwqK1JBFXURR5uvlpVpeuptZey/rK9dxy8i00Oht5+ODD02MVDg8rrjPICmaI6zQgnV9/NGRbYTrF1RVyZY24yrMPt/Vuy/g1R9xHgERrk1xpy6SnLFuQFddWt9TnUGKSiGs2x+GkwgW1FwDwfMvz0/5ZIPW3tg61sqxomeL38wx51NprJ2X7Hot0aX6poFVpqbXXcmDwQMLjMTHGjr4dJ/RmKxMXhQxZcVUiHZlYjmVSmmocDpCR3Xg60OPrQSNo0q6BhcbCCfW4trpbERGTNkQgpWVOm1VYAZ88rR7vwCKC0QDvf/L9CAiTJq4gkUx30A2zz4GuHTCUWXHRE/KgUWnQqZJ7p2RVLxsBTbJVOJupwvG+Mr8rQXENRUOEYiHCYX3C+i4IUlJsSquweaTH9Y3ON+j19dLubUTQuPCNsQo7A06cQafiuSQT1/FUsHRW4SJTEQOBgaSZ4C3ulrQq6ueWfg6AZ1qe4cZFN07pnHrXIBKCA0/D23+Cts0ZvWTQLa2zXd7EMSmiKGakuIZjYcXguoODB1ELWhyOEpbkn4I37GVH3474999pGWRldS5q1UjhYV6JDZVOUvnrc+opMhWxMH8hm7qVAxRf73gdlaBiXfk6Lq2/lCJTEQ8deCjl8UaiMV450MviehciInXWhbR1FeMMOpPCNp/a1UUoEuPK5SNJ2WvrC8izhel1qvnKg9u5f1Mr/3q7lXMXlFCZZ0qbpD2ryEJFoVQ8Hau4FpuK0aq0PLxjO50uPz++chEqlUAoGqLD06FYYCy3lCsT18DAuPvlq1dU0D7o54mdXXz+zFmcMqp4oAR5xvfYZOEdfTvo8HTE93UAZ1adyaqSVfx+++/p9Eh7zOymCgdniGsWMUNcpwED/gFMGtO4/UCZENeh4FDWiOvcvLkYNcYJEZ5Or3QRj042LDdLi2LH0PjEde/AXu7ff3/aAKpMIBPXbm8Xdr0dk9aELxTJ6jiclJ9tKqQhv4FXO16d9s8C4jfKpUVLUz5nedFytvclV1wnislYheXP39azLWHsS4urhaHQ0AlNXGX1NJNUyaHQEEaNMT7XcjRkApyWuEZSE9fjQXEtNBWmDZYpNBVOSHFVGoUjo9iUueI6ttdrMlhUYeeC2WuIuk7mwurLuevcuxTVu0xh09mktNzZ50oPZDgWxxP2YNFaFBU8efOe6ZigdMhqj+vw+R4RI1IQVdCdoLjK53wkosOkTVzfbTpbasVVawStGXwDPNr4aFwd1lj24wkkEsgjQ8MFWQXimmvIJRANpLXZx8QYnrAnLXGNibEEy7E75Kbf35+WuJZaSrll9S18cfkX+fyyz6d83nsGIS/c/z64/zp4+ma45zzY+ve0L4nGRHoc0t92LHH1RXyEY2FFK7YMuWildF20uFsoNVUAKnweidDJRGvAE6Sp38uK6rGzRi2oddLfWVYhVxavZFf/rvh1Mxqvd7zO4oLF2PV2tGotq0pWJc1DHY1tbU7cgQi5eV0ICFy/9FS6eyUSua0nUYR4dGsH9YVmFpUnnpcWU5C6vGKe2t3FNx/ZRTQm8pkzJCttXHFNYY+fUy5dO35fYoFVrVJj0xSzt7+Jj55Sy8ph95wc2KR0baUirn2+PsURZaNx4aJSvn7+PJ76wjq+eu7ctM+VUZ9Tn6S4Pt38NDqVjrOqzoo/JggCn1/2eZxBJw8fehjItlV4RnHNJmaI6zQg07mYmRBXd8g95URhGRqVhvl589nrSL1IjkWXpwu1oI5H/AOUWyXiKpPaVPjma9/kfU+8j//39v9jZ9/OyR30MHKMWjQqgf5ALyWmEqIxkUA4ltVxOOmwrmIdu/p2ZWUTOB62925Hq9KyIH9ByucsKljEUGhIsV9kIpBvVhNVoFaXrsYX8SWEdMmEe0nRiUtcJ6q4KoXqZPo+MilNNccVMh+H84t3fsH33vge/f7+8Z+cAXq8PWltwiAprq6gK+NZs/FgG4UNUYm5BGfQqbhRHI3BwOCEizSpcPN58wl1X4nRfQ2rS1fHH3f5wtz9ejPff3wP9719RDEYaCxs+mElsbgBrKUZ24W9YW/K4D45XCQbVmF/xI9KUCkWWSaKBMXVPwiII8FKjJzzYsyQ1Api06chrgCmfAaHunip7SWunnM1JaYy1Jb9OHyJQXZy/6HS2K54WFaaz/GFfcTEmKLNH0aNexpVmFEa5aSEK2dfyccXfVyxGPGegn8Q/n45NL0MF/0SvrQL6k6H/30edjyQ8mUdg35CQelv1O1JdCbI9+d0wToyqVW6lx9xH2F2Xi1atUBLr3Seyk6OLUek56+sSbxXGrRqbDYnWmxx98yK4hVEYpGkfc9Lhw6zZ2APq4pHZrPPz5tPj68nJXF8aX8vGpXAkNhIfU49Vy2dhVkoQYstoc+1zeFjU4uDK5dXJJ077pCT0+pr2HTL2bzxjTN559tns7hC+h3Jimuqz8+1eYlFLDy1K/H7d7/eTM+ABZvVxdfOGyGS6ZKzK6wVOAKOpIJql7crSdEdC51GxadPr2duifI1p4T5efNxBBwcHDwISO0Jz7Q8w/rK9UlOp6WFS6mwVNDsasagNmTFXRJHODDT45pFzBDXaYDD78jIeplpj+vYAedTwYL8BRx0HEyyMKVCp7eTYlNxQvJirj4Xo8ZI+1B7ytd1ebp4oukJzqg8A1BukJ8IVCqBAosed1hKt5Pn8h2NHleA0ypOQ0RMShmcDmzr3cbC/IVpg14WFiwEmHJAkyPgSNtbkgqrSlYhIPBW51vxx7b3bcems427MXsvw6A2oBbUmRPXFBvfTKzCsiKkOMdVk7niGhNjPHjgQR4+9DCX/vfSCfXAp0KPrydp2P1YTHSWa4urhWJTsSJRl8fujGc9nujop3Soyjdx3sIS/jMcGAKws93Jmh+/wG1P7OX+Ta3c8uhubnl017jvZdfZJcVVECS78OGXYNSIhlTwhD2KVnMYUQyy1eNqUBuyQqZk8huOhUcSgEcR1/g5H9MntYKktQoDmPN51ddKJBbhslmXsb7iNDTmRnqGEsPSjriPoBE08SLs2M+A9BbrdMFqMHJuj3YByI6BmcAlYKgH/noxdG2Ha/4Gqz4GOVVw/YNQfQo8+VUYUN4zNPV7IGbAqLYkKa7yuZ5OcZVJ7diRONFYlLahNmrtNSyryuXZ3QPk6nPp9knr4ZYjg+jUqiQ1E0BvHEQMjZzDy4qXISAk5FCEozG+8bSk5v39BQO7O6TzSy5Q73fsVzzelw/0saLGzp6BXSwpXIJZr+GSJeUEPVW80z1CXB/dJimZl41J2g1Hw3jCHnL0OdgMWspyjBhGFfxtOhtalVaxxxXAFe7DrCrgwc2t7Gx3AnDf20e47Ym9VNmqEDX96DUjVOKQ8xCQgrhapDCl0aprNBalx9tDmSX784rPqzkPrUrLI4ceAaTQS0fAkWATliEIAhfWXQhk2SYMEAnMKK5ZxAxxnQZMVHFNtTkVRRFX0JU1xRWkRTIQDShGkiuhy9OV1JQvCALllvJ4L4AS5Ij3Lyz7Aga1IanPYDIosunxxfopNhfjDUrEezrG4ShhQf4C8gx5GUXXTwXBaJC9A3tT9rfKqM+pR6fSpbUYZYJMekuUYNfbWZi/kLe6Rojrjt4dLC5cPOUE7HczBEHAorNkbBVOtfGV53JO1iqsV+tRCaqMQqJ6vD34I34uqbuEodAQ7/S8M+5r0kEURXp8GSiuwy6OTFXeFneLok0YiJPk8Uh3NhVXgOtPrsLpC/P07i5iMZFbH9uDWa/hyS+cyr4fnM9nz6jnkW0dbGxM/zPadLYRojT7PMk+2/b2uJ/vDXtTBqGZNCY0Kk12iGskkDUFQu7HDcfC8QTgeH8qI4Vcccw4HBjHKgxgKuBI2IVG0DArZxanV61HUIU56NqR8LQWdwsV1gpFBTkTxTVdsBooF2WaXc1oBI1ikvEJhcEjcO/54GiSiOqCS0e+p9bClXeBSg3//TQotMI090trWom5OIm4yq0vmSiuY6+LTm8nkViEGlsNHz+1ljaHH6MqP664bm5x0FBuSyB9MiKqPvz+3Pi8YJvOxry8eQnE9T9b2nGxC4PKRsBbyr82SaPj5uZJauU+x76k9w1FYhzoGWJWuY+h8EgbznWrKgn7Sun0thOIBBBFkUe3dbC6Lo+K3MTCXpzMp3BVybOLUymunZ5OFhZVE43BZXdsZOUPn+eWR3dz5rwibli5gkA0kLCG7xvYR42tRnFdKrdIhaLRokefv4+IGBlXcZ0Mcg25nF11Nv87/D8CkQBPNz+NWWtOOW/7otqLgCzbhGGGuGYZJ+4Ocxox4M+MDJi0JgSElKMzfBEfUTGatR5XGKnuZUp4Or2dCf2tMlL1Ksh4reM1yi3l1OfUU2uvnbLiCpBnEYkKPkrNpXQ6JbWpzH50FgOVoOLU8lPZ2LFxyv266bCnfw/hWDhtfytIqsXcvLlZUVwnu5FfXbaanX072e/Yz+1v3c5h12FWlaya0vG8F2DRWjJSXN0hd2riqpmaVVgQBMwac0bjcOS07IvrL0YtqBXHHE0EnrAHf8SfMMNPCYVGibhmEtAkiqLiKBwZMnEdr891IDAw5WCm0VhTl09tgZl7N7bwh1cOs73NydfOm8vCMrvUN3XmbKrzTXznv7uJxlL3o9v1doZCQ9LaUrceVFo4+Oy4n+8JeVJahQVBIFefm7Vwpmz0twLo1BJxDUVDoxTXEeIqF2TE2CSIq7mA1miAcms5GpWGlcUrQdTQ4kvsBWxxtyjahOXPgDTzYhlfcc0z5KFX6xM26C2u1GT5hMCRN+Cxz8GfzwTfAHzoMag/M/l59go48ztS4aY9uYjW3O/FatBQYS1LKlR1eSQim2oMF4xSXMdYheUgympbNWfPL2Z2kQWHy0i3txunL8TuDrfiFAR/xI83OkAsmM/BnpH1ekXxCnb07SAcDROMRPntCwfQ2xo5q3odDeU57OmUzi+bzkaltVJxT9Yy4CUaE9EapZ+roaABgMUVdkqG92X/3r6LX284RHO/lyuXJRdFZGU5HRnLM+QpKq6iKNLt7WZBUTUv3rSeL541m/MWlvDls+dw5weWU2uXriF5fjvAXsde5ucrjzmUHQ6j945y8WE6iCvAVXOuYig0xJ92/okNRzZwVtVZKYtwdTl1LC5YHCfYWUMkIPXgzyArmCGuWUYkFsEZdGakuKoElTSvMYUqIt84s0lca2w1GDVGxereWIRjYXp9vUmKK0CZpYwOT4diOFAoGuLtrrc5tfzUkWS3LBBXs1m6KZSYS2gdHkRdmZe8aZ8uLC1aijvkTqs0TxVy4nMmAUcL8hewb2DflIi0I5CZrV0Ja8vWEhEjXPP4NTxw4AFuWHADH5z/wUkfy3sFmRLXtFbhYfunNzS+4qpkFQYwao0Z9bjKxHVe3jwqrZUZuzFSQVYoxlNclfoAU2EgMMBQeCilzVLJmjkWoihmXXFVqQRuWF3NznYXP3v2AIsr7Fy9fGTzaNCquencuTT1e3njcGrV1aazISJKhEhvheq1cGj8FHNP2JN29JRdb89OONOwVTgbSCCuCoprvE9Z1GHUjQln0tvwhDyp1zxTPq1CJB6UZdAYMMVm0x8Z6cWPiTFa3a0piWsmiut4xFUlqKiz19HoGhkZ1uxqTukYeE9DFOGtP8BfL4J9/4OKVfCRp6HypNSvWXId6Czwzt1J32ru91JXYKbUXJqkuHZ4OtCpdAmZHGNh1VrRCMlOhNHEVaUS+PTp9bg9FlrdnfzwyX3ERJHLlyUTGrk4EQsXsLdz5JxZXbqaQDTAxs6NbGkZpCfYREzwcGrFqTSU2dnf5SYSlc7j+XnzFYnroWEiHFV3oxbU8cKdIAh8YIW0R/je0xv5zQuHWFufz0WLk/dq8szadAW7fKPy7GJn0EkgGqDUXIrNoOVLZ8/h9isW8cWzZ2PQquPr8aFByR7sCDgkopunnM8ht5mNJq7yfmo6rMIgtTUtLljMX3b9BU/YE1dVU+HOs+/kh6f+MLsHEQ6AZuIzvmegjBnimmU4g05ExIzJgFlrTrnJlW+c2fTbq1Vq5ubOzUhx7fX1EhNjKRVXT9ijeHPf0rMFf8Qfj/Kvz6mnx9eTUlnOFFq9ZAMqNpXSPigpSZW5R4+4yrNsleaXZgvb+7ZTbavOqPCxMH8hnrCHVnfruM9NBYffQZ5xchv5lcUrufOsO/npaT/lgYse4OZVN8c3pScyzFpzRlZhT9iTWnGVU4XTjLOR1dRUSphJY8qox7XJ1USOPoc8Qx519rop2/plBXW8HtccfQ4alSYjxXW8YBuT1oRNZ0trFfaEPYRj4awSV4CPnFLDhq+cxr8+fjL3fHgVKlViH+g5C4qxGTQ8sjW1QyWJLM0+F/r2gTP9tZ0unAmkzWq2wpmyZhUeXiOC0SB4hzfLo3pc5fNajGkVFdc4wVeAaMyjTaOiatQ9q1DTQFDVEbft9nh7CEaDKUlkRj2uYenzbdrUReVZObNoHJTuFZFYhNah1hOvvzUSgse/AM98A+ZcAF/eC9c/AMUL079Ob4XF74Pdj4AvcUxLU5+X2gIzpZZSnEFngquk3dNOmaUsbbuKIAjSLNcxBZ0WVwsWrSW+Ply2tJy5BZUEY17+s/Uwn1xfx/zS5L+3fP81CcXs6nDGH19bvpZ8Qz6PHHqEHe0uNJYDCAisLVvLwnIbwUiMw33S+j4/fz4dno6kc+5Q7xCCAK5IJ5XWyvgMZIBLFkq/w0+dlcvLN53Ov25crThlIRPFNd+Qr6i4ygGcSuIFSCppjj4nvp/cNyAJIqmCJeU2s3bPiBNhuhVXlaDirxf8lUcufYR7zruHNWVr0j7frrenvC9PGpEgZMmxMoMZ4pp1yFWrTIgHSBXbVJtceRHLpuIK0qKy37GfaCx92qVsu1FatGQrhZJdeHP3ZjSChpNKpIrqrJxZwIiyM1m4OYgYU1Osr6d1wEeBJTm8Yzoh/xzTRVxFUWRH7w6WFi7N6PnyzWGyduGYGGMwOHkFShAE1lWs44LaC+JhUTOQ1NLxektFUdp8p7q2DWoDKkGVlgCnswrLj2fS49rkbKLOXgdIVqk2d1vCmKOJQlY9x1NcBUGg0FiYUThTulE4MkrMJWkVV3mjmm3iKggCs4qsrJ1VQIEluapu0Kq5eEkZz+zuxhNUDsWLE9fgKOIK46qunlDqcCaQNqvHfY+rwS71Ng4jTkRiuqTU+LiNN4UaOmgw41GpqNKNqEtVRikvQO7Hl2dfpiqCmLVm1IJ6SoorwKzcWfT4euIunXAsTK3tBCKusZg06mbr32HdTfC+f4I+9bmahJUfhWgwYTxOIByl0+WnpsAcb0UYXaxqH2pXDNwaixx9MnE94j5Cta06HkCmVgl8dLWkas4ui/L5M2crvpdsk11YVMf2thHiqVVpubT+Ul5tf5Ut7S2Y7I3xrIyGMul6jwc0DSuUBxyJs9EP9XqozDVxZKg5qehRaCpEJagwGN3UFKR2XWSquCrNLpZTm1ORSkEQWJi/ME5c5X9TWYVBCmhKsAp7usjR56S8j2UDWpWW2bmzpVDJY5HWHfHPKK5ZxAxxzTLixDWLims2w5lAWlT8EX98ll0qyJWwVIoroGibPew8TKWtMr4Q1dvr449PBd3BPUQDlbj90Dbooyrv6FawLDoLpebSeLR6ttHibmEwODhuMJOM+px6DGpDwkiaicAZdBITY1nfyJ/oyMQq7I/4iYrRlBtfQRDSthGAZBUWEFJaOM1ac8ZWYXlTVGevIyJGaHNPfsxSpsQVpM1Xrz8zxVWv1qetyhebiuM2ZSXIwS3Z7HHNFFctL8cfjvL0ri7F7yepfAWzIac6LXENRUOEYqG0iqtdb580cd3X5eYL92/jl88dwB30ZY24ymnp8R7XUf2tMEpxFXWK43AgNXFtFaSNd6VmZCNfZ5uFGDGzseMNYMTWmIq4CoKQGJalAJm4pisaxAudg40nZqLwlnvg8Itw4c/hrO+AaoLbzZIGqD0N3rpTsloCrQ4fooikuA6vBaPtwh2ejnhybToUmYqSbMatQ8n28QqrtPf57hXliqFMIBHeXH0uKyvLONgzlDD+6vLZlxMVo2zx/omoroVTyk8BoK7QgkGrive5zsqVzhU5kVfG4V4P9UUGWt2t8eKiDK1KK/0cHuU1RYasuKZz7hWZiojEIvE1UkYmauiC/AUcdh4mEAmwz7GPKmtV2oJOubWcjqGRNrNOb+e0qa3HDcIzPa7ZxAxxzTJku0WmZCBdAmmcuGZZcZWJpGy/SwWZlCoqrgpN9jKaXE0Ji2yZpUxKFp4CcfWEPHT4DhH11rG/a4hWh4+qo9jfKmN27uxpU1y3924HyJi4alQaGgoa4n2xE4Vs0cyEYMwgc1i046cKj5dKGn+fNATYH/FLAW8pKsiZWIUdAQfOoDNBcYWpuSN6fD3kGfIyso0XGYsysgo3u5upslWltQAWmYrSKq7ypuxYFGqWV+VSlWfi8Z3Km8wkq7AgSKpr8yvxTftYyEWNdD2ucjiTUhZBOoiiyPf+t4dndnfz+5caaR4YxKjOzsZLHq0WjAYlxdWsTFyJaTGN7XEdJzipTZRmAlcJI+pGvsVAxDub1zteIxgN8mzLs8zLm5e2D3K8ebFDoaF4anMqjG4tSTeD+D0Jdyds+D7UrodVH5/8+5z2NfD0wLZ/ANAynChckz+KuA4Tt6HQEO6QO6NgnVp7LS3ulvh1EYgE6PR0Jv195DFbff7U60qHp4NKayWLK3KIxkT2dI4UPOrsdawsOpmw/gB52kouqpP6K9UqgfmltvhzC42FWHXWhD1SJBqjqc9Lcb6XiBiJr82jUWYuSyLgYzEYGMSqtaYNBVMqAoBEKo0aY1qb8YL8BUTECAcHD7J3IHUwk4z6nHp8EV+8mNPlGX+G67sekZke12xihrhmGRO1CqfbnMo352zPlKqyVQEjYQSp0OXtIt+QrzhP1KazYdVak2a5hmNh2ofaEyrLapV6ysnCW3u3EiOGPjKbN5sG6HIFjmowk4xZObNodjVPyUqZCvE5qBMI8FhWtIz9jv0Z9TKORaa9iDOYGCy68RXXTKyGZq057d/VF/alTXo1aUzjpgrLCcLypki2Mk7lWu3xjj/DVUappZRub/e4xCpdorCMAmMBzqAzZQvEsSSugiBwwaIS3mjsx+VPXjvkNT5B5Zt9LoR9sO9xxfeUz7F0qp9dbycqRuM9mZnijcMDvN3s4FsXzuO7lywkGA3iD2VnuyAIAjqVjlAsJPW4KiiuakEHqDDpJ2YVbo14UIki5dGR8ynfoifsXIUr5OKObXewq3/XuAEtdp19XOKa7vcOknXdorVwaPAQza5mcvW5ace0vKew8TfSZv3iX0lFmMmiZh1UnCS9XzQcD2WsyjNRbCrGqDHGHVByET0j4mqrxR/xxwtdh12HERHjyqeM+JgtX+re+R5fD8XmYpZUSNfwjvZEpf6DtbfhOfADfrL67wkF/YVlNvZ2uonFRKndIGdWwrrb6vARisYwW6Q95VjFFaT1c1ziGhwc97yTg5HGOui6vd2UmEvS2msX5kttQvfuvpcOT4eU5J0Ga0qlHtM3Ot9AFEVpcsU0BTMdF4hFIRae6XHNImaIa5bhCDjQqXRp7VujkY64ukIu1II6ZWroZGHX28kz5I1LXDs96ReUKltVvGomo83dJlUHxyyyldbKtONzxsM73e+gVWlZXrKUZ/d0E42Jx4y4RmIRjrjS/+4mg139u1hUuGhCc1CXFy8nKkbZ1b9rwp8n9wbNENfswqK1EI6FJUUpBTIlrmnH4UR8adeGTHpc5X4/+Xo1aU2UmkunpLh2+7ozPqfKzGX4I/64nU0J4WiYDk/HuDbLAmNBvG9bCXJP27GwCgOcv7CESEzkhX3J6k3cKhwatemtPxNKl8LTN8PQqNdEI7D3MTyb7wLAPNgmhX8oQP5ZXYHMR+KIosgvnz9Iqd3AdSdVccXycgRViNZ+5f7cyUCv1o+kCpsTi7z+iB/NsGJqGmPPHC/xtzXooDQSResfOQcKzDqivjqqLHO4d8+9CAhcUHtB2uOz6q3jWoXHc0LJZOTg4EH2OfadODbhSBB2PgjzLoL8+qm9lyDAKV8EVxs0bqDN4cOq15Bj0qJWqVmYvzDeKtMxNExcM+hxlf8WshIu28dle7cMnVpHniEvbQtCr6+XIlMRRTYDpXYDO9qcCd/f3e5BQENDeaIAsbDMzlAwQsfwaL/6nHoanY3xIt6h3uG1X9ObcMyjUWYuo8fbkzavxBlwxmfXpkIq4pqJGlpiLiFXn8uG1g2UW8q5YvYVaZ9fYa2gylrFm51v4gq6Mhqd9q6GnJI+o7hmDTPENcvo9fdSYCzIuAHcok0d5OIOurHpbNPSTF5jq0kinWPR5U2/aM3JnZPU7ynfCMYusmWWMjo9nZMe3bKpexOLChZxck0JQwFpA3U0E4VlzMmdA2Q/oMkf8XPYeThevcwUSwqXICCwtXfrhD+zx9eDSlBl7A6YQWaQrZvp7MIycU23+U23NgD4w/60gRYmrWncHtf+4XEko+3idfa6SY/EEUWR9qF2Kqzj95nByIYpXZ9W21AbUTGakeIK0O9XHjvjCDgwa82KDpKjgSUVOZTYDDyzO1m90al1GDXGRAusWgNX3gUhL9x3FWz4Hvzv8/DbZfDQh/Bs+hMAlg3fhztOAkdysUG2+KUrDIxFY6+HLUcGuXFdHQatGptBi1YToaU/pKgWTwZatZZQJCjN81RSXNGjU6vQqBO3KONahX1dVEaiI2N2gDyLDhA4peAqAE4qOSluAU2FTBTXTJJHZ+XOYmvvVvYO7OXMKoWZpe9FHHgK/IOwLEuj0eacJ50j2++jbdBPZd5Ie8SigkXsc+wjHA3Hk2oz6XEdS1wbBxvRq/VUWauSnltiLkmpuHrDXrxhb3z9XFKRw452Z8JzdrY7qS+0YBmT+DunWBI3DvZI94JZObNwh9zx9atxmLi6Yx0Um4oVWwJKzCVExEjakWLOoHNcxdWms2HRWuIpwjIy6T8VBCEeFPnlFV/OaH1dU7aGTd2b4sFWR01xjUWl+div/1r62vgb2Phb6euN38Ebv4c375C+3rkXglObhAGMtHrM9LhmDTPENcs47Dw8ocqqRWfBH/ErWk+dQWfWbcIyqm3VaXtcRVGky9uVdkGZnTsbR8CRsFGUlRol4hqKhZKa/zNBn6+PPQN7WFu2NmEAeFX+0SeuNfYa1II66wFNBxwHiImxCRNXq87K7NzZbOuZeJ9rr08qsqTr05rBxCFvaNOppZn0uI4XzuSP+Me1CvvCvrQ2XEfAgV1vTzgHKq2VSS0AmWIgMIA/4p8wcU3nxpBV4UwUV0hPXMdTHqYTKpXA+Q0lvHKwD69CurBVZ01UXAEK58Jld0ibn42/hX1PQMEsuO5+fNdKaavmM2+FgBvuOR/6E8Nd5PvHRAKant7djSDAxcMzIUVRJCaEiUS0PLcntWVyNERR5I3Gfh7brvx31av1hMIeiEUUe1zVKCfGGzVGNIImteI61EYlGin0aRj5ZmkjXaY9iXOrz+WjDR8d9/jHC2dyh9wZEddlRctQCSq+cdI3+L+F/zfu898T2HYf2Mqh7ozsvJ9aK43GOfAMzv5uKkeFMjYUNBCOhTkweID2oXasWmtGmSAFxgIsWsuI4uo8RJ29DrUq+ZwrMZWkVFzHBtEtr87hyICPbpdEVgLhKG8cHuDk2uT2hFlF0vlzYJi41udI6rRcFD/UM0R5jpG2oRZFmzCkVkpHYzA4mLZHVUappTShgBiIBHAEHBn1n14952reN/d9nFt97rjPBWkGvD/i5wdv/gBQtkFnFQeegcc+B79dCv+6FjZ8V/p6/lZ4/jvS13PfhudugWe/JX098SX459UQHH+0XVo4hu3futRZBDOYGGZ2rFlEJBbhsPMwa+annxM1GrKl2Bf2JZFUV9CV0YIzGVTbqhkIDKSsHA8EBghGg2kXrdm5UvjEocFD8U1js6uZIlNRUnVQTibu9HTGn5spXm5/GYAzqs6gymJHp1YhIlJiy07K5USgV+upsdXErUXZgjzSZqLEFaTN0eOHHycSi0yIhE6kF3EGmUO+pjNRXKdqFU63STNpTYiIBKKBlATXGUy2kVVYKxgKD+EKuiZcOJMJb6W1MqPnpwoFGY3xZrjKkJ0DqYjrQGBgwmtPtnHJkjL++kYLD2xu42OnJhJxu96urCQuvkb6ikVh1Mba2/QUAKa5F0LtORJxffRT8LHn4s+T/7bpSNhYPLO7m+VVuRQNr6+RWISYGMWmN/Hsnm6uWZn+bzsUCPOxv73DpmapSGnSaThnQeI6o1PrCMnXh4LiKpCcKAzDib8pgpP8ET+uoIsytSlh9meuSQqlGfTG+MU5vxjnp5dg19sZCg0RE2OKrRtDoSHFsJyxuKTuEk4rP+3E6W319MLhF+DULyecq1PG0uvhrTtY7noeYf6n4g8vKlgESG02HZ4Oyq3lGTnUBEGg1l4bL4odGjyUcr5nkamIzT2bFb83Nidi3exCYD+vHurj2pWVvHaoH384ynkLk62wdqOWEpuBQz3SdSDblA87D7OmbA2HhhOF9zobuXbutYqfL++r0q2fmViF5fcarbjKpDzVDNfROLv6bM6uPnvc58k4qeQk1IKaA4MH+MZJ34iT9mnB9n/Bfz8NxjyoWAnn/ABmnQ2CCuJFXXH4v8WRxxo3wCOfgD+fCfMuhGU3TNz6Hg5ILhlLCcy9MJs/1QmNGcU1izjiPkI4Fo4TukwgEzylgerOoHPaiKu8CZSHZ4+FXHlLp7jKttnRJG5sorAMefFLVxlMhZdaX6LcUs7snNkYtGoWV9ipyDWhVh2DeVzA3Ly57B/cn9X33NO/hwJjwaQSflcWr8QX8U14LE6Pb4a4TgdkMprRHEjt5BVXX9iX3io83P+a7j0GA8nV+HQzmsdD25A0RidT4hq3qKVZF1rcLZJCMk4YjjyCTA7IG4sB/7Enriuqc1lTl88fXzlMIJzYl2bX2ZMV19EYQwRkG7hZY5aU2Qt+Ch3vwKY/j7zncOFh7MzKVGgd8LG3y80FDSMbbX9U6sGbV5zPq4f6U86ilfHzZw+wucXB9y5ZwPxSG998ZCcDnsQeXK1KSyg0fF4q9LgKoi7ljG6bzqZI8OMp6TprglVYo1aRa9Li8IaSXtPl8vORezfxzUd28vqhkdfYdDZERMX7MsBQeCjttStDEIQTh7QCHHgaxBgsTN/nOGGUNBAuWszlwisJ2RYl5hIKjAVs7t7MYefhjIKZZNTaa2l2NeMMOOnz98VToMeiyFTEUGhIMehubDL/vBIrhVY9rw2fS8/u6cZq0LC6TrkdZ06JNW4VzjfkY9fbaXQ2Eo2JNPZ6KMhzEIwGWVy4WPH1cm9oKuLqDXsJRAPkGccPpJPbuWTEp0pMQ+KvRWfhppU38bPTfsYH5n8g6+8fR+ML8Nhnoe50+Op++MC/pXNTb5UUUL1l+MsKBps0U9qYI30tuhqu/bv0vTd+B3eugZd/nLl9OBqBJ78Kffvh8jvANDN2MFuYIa5ZhGwflQldJpA3uUqby+m2CgMp+1zlylu6RSvPkEe+IT/+c4uiSLOrWZG4xhVX78SIqy/s4+2utzmj8ox4JfV7ly7kR1cumtD7ZBPz8ubR7e2OD/bOBvYO7GVh/sJJ9TOvLV+LRtDwYtuLE3pdr6933F6vGUwc482aBIm4GtQGtOrUIwosOqnHNVVfuC+SPlVYLor5w6mThQeDg0lhRVMlrgJCxhtIQRAotZSmJ64ZJAqDpDCbNKaUimu/v/+46Of+4tmz6RsKct/biUXD8eypYyHfM+LFi0VXw6xzJAvc3v8B0v1FLagTrcKRIOx4ULIeb/lbwridZ4etwKMVosBwuMjisgJCkRgvH0g9vmhHm5O/v3WED62u5sOn1PKr9y3B5Q/zh5cTU6r1aj1B+Z6nNMc1pqy4wjBxVbi24iRCn5dgFQbIM+sY8CYHWP36+UO83tjPkzu7+OhfN9M8PG4lXQiUKIp4Qp6MrMInHPY/CTlVUNyQ9bfuqr2CBlUL84SRYERBEGgoaOD5I8/T5e0aN3RrNGrttfT6etnRtwMgKVFYhkxK+3zJfaRjiasgCKybXcDrh/oIRWJs2NfD2fOL0WmUt9pziiw09nqIDicL19vrOew8TMegn2Akhsoo/axLC5cqvt6kNZGrz025fsrHXGhMPfpJRpm5DE/YEz/n5fDG6RpV88EFH+T82vOn5b0BiITgqZsgfxa8777JhSPNvxhufAG+vEdSXV/+EfxqITz6aalHtm2zRFDHwueA+6+D7f+E026WFN4ZZA0zxDWLODh4EI2gmVCPazzIRcESOJ1W4UpbJQJCymThTBRXkEi6PDS7dagVX8SnaPuw6CzYdLYJK64bOzcSioUSgi0ayu0pK5hHA3Pz5gJwYPBAVt7PF/bR5GqalE0YpI3cipIVvNz2csav8YQ8eMKemRmu0wDZvptKrYFhxWacja9sOU410mY84iorrukCmgYDCsR1OJVzMu6ItqE2SswlGc1wjX+euTxtQavZ3ZzxiKgCY4Gi4hqOhnEGncdccQVYXZfPybV53PN6M7HYSP+xXZ8+EGgs5FFJ8WRpQYAr/ggli+ChG+D1X6FCwK63jxDXXf+BXy+GRz8h9XU9/gX44ymSLU4UeWJnJw3ltgRVSyauc4vzyTfrFMOlZPz02f0UWPR89TxpjZxXYmN1XT6vHEzc9OvUOsLyeT2mxzUQCSDGtJi0ym0PVr1V8feU0G/oSySu+RY9A55ExbW538t/trbzwdXVbPjKevQaFbc+thtRFNOGQPkjfqJiNOvz1d/1CHqg6WWYe9HURuCkwM7ccwmJauZ0JY6HWlO6Bo1Kw0/X/5Tzas7L+P3k0V8PHngQIK3iCijOm+7x9mDVWRPW4dNmFzLoC3PrY7tx+sKctzB1cXhOsZVgJBYf87OoYBF7Bvawu0v6LHfsMEXGorTOqFJLacr1Uw5tSjezePT7wMjer8vbhYDw7nVlvXOPFFh37u2SqjoVWEvgmr/CjS9KvduHX5QKhHefDb+YC09/A976g/T16s+lsLyml+DiX8OZt2Tjp5nBKMwQ1yzi0OAhauw1E9q0xYNcxvTD+SN+AtHAtNmM9Go9ZZaytIqrVWsdd3M9O3c2h52HicaiPH/keQDWla9TfG6ZZfxh2WPxUutL2PV2lhUtm9DrphNzc6VN2X5HduzC+xz7EBFZWDA54gpwRuUZNLua0wZujcbMDNfpQ0bENYNUUllJS9Ur6w17FZMmZRiHUwxTEVdRFCXFdUz/k01nw6pLntGcCSaSKCxjbCjIaAwGBnEFXRkpriAR1/5AsuI6EBiIf/94wPUnV9Hh9PNW8wjJTtnjmgLesBejxpgYKGMugP97HBqGU4gf/hj2WAxn5xb6/vUJePhjRG0VcMN/4Zsd8MFHIBqGf15F8I5TiHZs49IlicVKuXBi0ho5c14Rrx3qTyDcMvo9Qd48PMB1qyqxGUacBOtmF3Co10OXa6QAo1PrCMljIhQU11hs8lbhYks5BFzSzzWMAouOgTFW4d++cAidWsWnT6+nyGbgpvPm8tqhfp7b2zMyV1fBup1JsNoJicMvQDQojcGZjrf36NkQW4H90KMJf9vr5l3Ha+97jfNrJqbeLSxYiEFt4LWO18gz5KUs4qYjrr2+3qR76KmzpfP5gc1tnDIrn9Pnpi4Ozx6TLLyuYh3hWJiX294AoNW3j8WFi9M6sUrNqddPWXEtMo5foB7rtOn0dFJoKkzrCjpu4R+EV34sWYRnn5O99y1fAdf+DW46AF9rgqvvgeq18M7d8Mw3pK8XbwN7BXziZVj5kex99gzimCGuWcTBwYMTsglDasVVtoxNl+IKklVm9MDr0ejydGXUlD87dzbBaJBGZyPPtTzH4oLFKV9XZi6bkIoTjoV5pf0V1lesP66Sb/ON+RQZizjgyI7iuqdfCmaSI+UngzMqpQTHl9peyuj5sjoxQ1yzD6PGiFpQp1XPMkkllRVXpTaCUDREJBZJS1zH63H1hr1EYhHFuaYVlopJW4Uz7W+VUWYuYyg8pPj7kgtrmbpY8o35ilZhWYU9XojreQtLsOo1/GfLSHHAprMRiAbSzv8djZSKu9YIV90N678Bex4l192Ns28vhQcf5J7I+ZzU+VW2aJZKKsSss+Bzm+HS3xMa6udh3fd5v/cfknLgkIJrAlGJYBrUBk6qzcPlD4/MmByFZ/d0ExPhwkWJ678UWENCD6lOpSMYDYHOAtrEkD1/xE8sqp2UVdisNWO2DK9pvpGiQKndSPugj3BUst17gxGe3NXFNSsrKLJKn//B1dWU2g08tLktreIqF6TG67k+4bDvCTDmQlXm4ZQTQavDxwv6sxF8/XDoufjjKkE1qb9FibmEV973Cv+44B/85dy/pCSH4xHXsYS3wKLntssWcsf1y/nnx07GoFU+jwFmF0v3gEPDxHV50XIsWgs7B96k0B6mw9Oesr9VRqm5lC5vl2J6vKy4FpjGX/fGBuXtd+zPuGB43OG1X4DfCef+cFrUf0DqzW+4Ct73D/hGK3y9ZeTrxpck58sMpgUzxDVLcIfcdHm7JhTMBKkVV9naNZ3EdW7uXJpcTYRHVS9ldHo7432p6bCmdA1GjZHvbPwO+xz7OLcmdRy63PyfbjzHaGzr2YY75I6TsuMJ2Qxo2jOwhyJT0ZQ21WWWMhbkL+DfB/+d0cZ3hrhOHwRBwKazTVlxlUmpEvGUH0tHXOXvyZbSsZADe5SIa5mlbMLE1Rf2MRAYmDhxTTPLVXYQyLa+8VBgLFAkrvJjxwtxNWjVXLyklGd2d8dH48T7KjNUXdMq7oIAZ3wTvtlOIP90NqtL+cbcp6m8/rdYzUZuuPtt3jw8TOw0elh+Ax/V/ZwD+gasb/9KUg7uXA0v/pBA21vSMWsMnFwrtWhsakkea/b0rm7qCszMK0k8r+eVWCmwjATWwLDiGg1KFrwx8Ef8RCLatIqrnPg7GnESYR0mzkMjluZlVTkEwjH2dkq/29eGexAvaBgh2WqVwKVLynjlznh63gAAS7dJREFUYB9iVCoIKBHkTBLBTziEvFJ/6/xLpPnD04A2h4/2/LVgLpKSYrMAk9bE0qKlafdtFq0Fo8ZIrz8z4gpww5oaLlpcOm5mhUWvoTzHyP5u6ZzSqrWsKVtDd3gbxUXS+bukcEna9yizlMUTtceiz9eHQW3IKEgsz5CHQW2gw9NB+1A7BwYPcFrFaeO+7rjDYAu8/SdY+oGjRx61RqloI39NF1meATBDXLOGxkFp9la2FFeZuE5XOBNIIUORWITDrmTVNVPFtdhczM2rbmafYx8A51SntmWUmkvxRXwZ93G91PYSOpWOtWVrM3r+0cS8vHk0O5szVkfSQQ5mmiq+tPxLtA21cfeuu8d9rjyXrsg80+M6HbDqrGkJyESIq1L/ezyYR2NK+p4M2Wqcyio8GJSIq1JxrNxSPqEiE4wkCk/UKpxulmuzuxmtSpvxgPp8Qz5DoSFC0URb6PFGXAGuXlGBLxTlH29JOQOyypdpQJMv7EtbuAAYiunY22EGrZvvXr2CcxYU89An11CWY+Tjf9vMznYnsZjI7188xOY+NTtOv1dSD76wHWafC6/+jMDztwJg3HY/lVaRYps+PupGhsMb4s2mAS5YVJK0WZcDazY2jliMJeIagpzqhOdGY1GC0SCRiCal4mrX2xERk66LOImwDweDuUbU7JXVUqLn5mHC/dyeHnJMWlbVJBZtLl1aRiQm8sYh6ZpR+lvIxHWmx3UUDjwNYa80b3WacMThpSLfBkveBwefSQrgmi4IgkCRqShJcY3EIvQH+qecE7G0Kod3Wgbja+1pFacRVbno0NyDVWdlfv78tK9PF3zZ65dmtWc6ImhR4SKea3mOZ1qeAUjIFnnXYMP3QKWZ6S19D2OGuGYJ8/Lmcc9590y4F9OgNqARNCmJ67QqrnnKvZpDoSGGwkMZKa4AV82+irOrzmZN6Zq0G8yJpJWKoshLbS+xumx12pEfxwrz8+cTESPsHdg7pfcZCg3R4m7JCnFdU7aGC2ov4C+7/jJur2uvr5ccfQ569SSS9mYwLqw6K+5weuI63sY3nVU4PgplClZhWXHNMyTH9JdbyglEA/He0EwQn+FqmZjiWmGRiK5MfEej2dVMlbUqsY8zDWRiOjagSSauSj/rscLyqlzOXVDML58/SGOvJ6M06tHwRXxpCxcA973dit+XC0KU/mHFqMhm4J8fO5kck45r/vgmq27fwM+fO8jFi0u5ZlWVNBIir1aywH31IP71NwFg2Pp3hLvO4LJSJ5ubHQlFjUe2thONiVy0SHn9X1ufz4A3xOE+6T6nU+kIiRHITSSusi05FNZg1ikrd6lsvPF+Q/vw+TeKuJbYDVTmGXmnZZBwVEp7PWteMRp14hZoQamNWUUWntzZj0FtUPxbzPS4KmDng2CrgKrpKTJ7gxF63EFqC0ySkhaLwM6HpuWzlFBkKkpKFR7wDxATY1N2LZ1cm0e3O0D7oNQDXq5bhhjTUWys4h8X/CNtAB8khyqNRr9/YsT64w0fp8fXwx93/JE5uXMm7J455mjbBHsehbWfB1tm+9cZvPswQ1yzBJPWxKqSVRO+mQmCgEVnSboJuwLT3+NaZa3CqDEm9WrG53dloLiC9DP88vRf8qdz/pT2eekW2LHo8HTQ4elIGfR0rHFy6cmoBTWvtL0ypffZNyAp1VMJZhqNm1fdjEFt4Pa3b0+rlnV4O+Iz4GaQfdh0NoZSzHsTRXHKiqts/01LXIcLPqlSidMprrJqOpGAps09m9GpdBNKVQfIMeRQYi5hz8CepO/tHdjLnLzMXSwycR1rF+7392PX2ycUnDfdEASB269YhFmn5qv/3oFFMzHF1Rv2pi3qBcJR7n69mUVF0niy1qGR8TsldgP/uvFkrl1Zydnzi/nltUv43fuXJffjWYsJFEg2SsPlf4SAk6903kTI3RvfaIejMe7d2MJJtXksKFMuxsiPHxju5dMjEEJMUlzlczUSTWMVHib4o4OTYmKMPl+ftEk35YPGAK7EQsiq6jzeOTLIm4cHcAcinKuQ9ioIAhcvLmVTswOL1ppWcZ0hrsPw9kvzMhddBarp2VK2DEjFt9oCCxTNh7LlsPVvEFMeFZZtFBoL4+01MsaOwpksTqqVimmyi2FnawzPoW/wp7PvVZzQMBZyb6qS4trn68soUVjGmrI1LCpYRDAa5KyqszJ+3XEBUYRnbwFLMaz9wrE+mhlMI2aI63GAfEM+jkCi9epoKK5qlZrZubOTFFe5OX8i87sEQRjXjiJXJrt9qccpyNjZtxMYv7/jWMGms7G8eDmvtE+NuMqb9akEM41GgbGALyz/Am91vcXTzU+nfF6Ts0lx3u4MsgOrTnlkB4yM05hKOFMmPa46lQ6NoJlUj+tEZ7mKosiLrS+ytmztpBwSC/MXJrkXerw99Pp6WVyQPpxkNFIR14HAAAWG48cmLKPQquf7lzWwo83JUzskkqSUZKuE8VKlH9/RSd9QkBtXrwKSFe3qfDO3Xd7AT65ezJXLK1Ku3/I4HEPd6fChx9BFvfxAe2+8Z/WpXV10OP18Yl3q9aS+0IJaJXBguJdPFw4QEoQkxVUmrmKaOa7yfWS0AuYIOIiIEYlECIKU6ulKLLqsrMmj3xPkKw/toNCq57TZyhv6dcOpsBosifNvhxEnrhn0DZ4Q2PMoiFFYdO20fYQ8X7e2YPh8X/NZ6NsPex6Zts8cjWJTMX2+voRicML4pSlgTpEVu1EbJ66vHeqjLq+Q6rzMzq8cfQ5GjVEx+LLP35fRDFcZgiDw+WWfx6gxTu+M1enAa7+A9k1wxi1TH38zg+MaM8T1OIBSoIgz6MSkMU17FPm83HkccBxIWJDl2a4T7VUbD7mGXLQqrWI631js6t+FQW2YcNjV0cT6ivU0OhsnNTZExt6BvZSZy7JqYbxmzjU05Dfwq62/SgowAWnD2+XtyqiaO4PJwaZPHc6UqdUwk3CmdDYyQRAwao1pe1x1Kp2i3bTCWoFGpeHQ4CHF14ZjYf61719x8rDPsY8ub9eke6IW5i/kiPtIAtnf1b8LgEWFmQds5Bul8KCxI3H6/f3x7x1vuGRxKRc0lHDPq9JGONNwpvF6XP+zpZ3aAjPnz5+DQW1IUFwnApm4GjVGSe1a/3UuVr9N/4u/IxCO8oeXD1NfaObMeak38Aatmpp8U5y4asM+QgiQW5PwvLg7IKbDmMIqLBNXuU8fFNQvBeIq97O6A2HuumFFSkV3UXkORq2aaMSYkrga1IZ355iQ6cDOh6BoAZQ0TNtHtAwT15qC4bVq4ZVQ3AAv3Z4wGme6UGQqIhQLJSjwclFPLvJNFiqVwKqaPDa1OAhGorzd5IgXTzKBIAiUmkvp9iYKAr6wD2/YOyHFFSTV9a3r3zo+C9sBFxx6XlL45T2rKMKmP0tjaBZdC8tuOLbHOINpxwxxPQ6QZ8xL6iVzBV3TqrbKmJs3l6HwUILNZO/AXkrMJVnvB1MJKsWQAyXs7N/JgvwFx9UYnLE4vfJ0gCmprnsG9mTNJixDrVJzw4Ib6PZ2s7Vna9L3m5xNADPEdRohK65Kdu1MrYZatRadSpc2nGm8cB6TxpS2xzXHkKOotOnVeublzmNn/07F1/5tz9/40aYf8ekNn8Yb9vJC6wuoBFX8mpgo5B5v2ToP0hqgUWmYlzcv4/fJN+QjICStMcczcRUEgdsub8CiM4Mo0DmUWV9xuh7XNoePt5sdXLW8HJVKRYW1QrGHOBP4oxKZlPvhVad+iZ6S0/lC8C4e/+Wn2N/t5ubz56FSpXfczCuxjViFgx5CKgHRXpX4WaMV1xRjRPIMeagFdYJ1M2kutb0C3IlugfpCCxcvLuW31y1jWVWyy0CGTqNiZU0uQz69InF1hVzTGpr4roKjWVK5Fl0zrR/T1O+lxGbAJBczVCo48zvgaDoqva4y+Rt9znV4OrBoLVkJ6Tq5No/mfi//fqcdfzgaHyGVKUotpUlWYXkUzkQUVxkq4TikBnv+Cz+bDfddDf+8Ev55lZQe/MAH4KmbYNY5cNkd02ZXn8Hxg5m/8HGAVIrr0bg5ypvC0RvGbKXcKiET4hqOhtk/sH/c+WXHGtW2ampsNbzc9vKkXu8KumgbasuaTXg0Tq88HaPGqGgXllOk6+0zxHW6YNPZCMfCiqnT8VRS7fgbHovOomj1zSScCaQ+11RWYWfAmbY4tbhwMbv7dxONRRMebxtq4487/sj8vPk0Ohv54FMf5N8H/s2K4hWKtuNMIBdvdvfvjj+2q28X83LnTShATKvWUmwupmMokbT0+/uPq0ThsSiw6Ln3wycjxow8uuMQ/Z7E82Zb6yA3/XsHH//bZj7x93f41D/ewRP2srnJi9MXSnq/h7e2IwhwxXLJNVNpraTNPTniGogE0Kq0I0VEtYbCj/+HJ3XncY3/IR4r/RvnzR2/yDmn2Eqrw4cvFEEXkK6B8BhL34jimnqOq1qlptBUqEhc44qrrUIahxMZ+d2oVAK/v3455zeM39u/ui4ft1fHYMCZ9D1n0HlUisrvCuz6j/Tvoqun9WNa+r0jaquMOedBwVzY+vdp/WwYZU/3j9jTOzwdlFvKM0rsHQ9nzi9Cp1Hx7f/uRqMSWF0/sSJbmbksKTtEviYmqrgel9jxAPznI1C2FD70Pzj/J1IQ09M3w+EX4JwfwPsfAM3xk2Ewg+nDDHE9DlBgLMAf8SdsMI+m4qpVadnRtwPIbsqtEjIhrgcGDxCKhVhUcPwPcD698nTe6XknaQ5vJpB7+qbjd23SmlhfsZ7njzxPOJZopWpyNqFVabNuBZ/BCOLJp1OcA2nWmqekuJo15pRWYUfQkXaNWVy4GH/ET6OzMf6YKIrc/tbtaFQafnfm77jtlNvQq/WUWkr50IIPjfvzpIJdb6fCUhHv+Y7GouwZ2DMhm7CMCksF7Z4Rm6gv7MMf8R/XxBVgSWUOxeZcPOEh3venNznQPURLv5eP/nUzV9z5Bs/u6abTGaDV4aOx3wGI7GoP8NNnE8P1AuEo/36nnTV1+ZTnSFbyKmsVbUNtiq0D4yEQCWDQGBIeU2m0FL3/DzxTfCNLBp+D36+AF2+HN++UNpm9yTOu55ZYEUU41ONBNxw+GIwlkm7ZliyKupRWXpCIxGircI+vB5WgIt8wvOG3VwAiDCX3/WWC1XX5iFETrqAr6XfmCs4orgCEA7Dt71KScE7V+M+fAloGfFIw02gIAiy9Htregv5G5RdmCTL5G7136RjqmLJNWEZ9oYUXvrKeD62p5tOn12PRT8xpVmYpYzA4mLCHlMWQySiuxxUOPAP//QzUrIMbHoW69bD6U/C1Rri5Gb7eAqd8cdrmB8/g+MMMcT0OoDTC4WhVdfVqPQ0FDWzr3QaMSrmdRuLa4+tJm3grBzMd74orSH2ukViEjZ0bJ/zabAczjcX5teczGBzkrc63Eh5vdDZSa689rm3Y73bIxFWpz1Umsxbd+AESZq0Zb0i5x1Wj0oybkjue4ppOIZVDkUbbhZ9peYaNnRv5/LLPU2wu5pL6S3jg4gd48OIHJ20TlrGwYCG7+3cTE2Mcch7CH/FPqnhVYa1I6Ds/Hme4pkKROZdFVVp63EHO+/WrnP7zl3mraYBvXDCPN795Fk99cR3PfOk0HvjUcgBOri7l/k2t7Okc6b373YuH6HD6+dyZs+KPVdmqCMVCGbVpjEUgGsCoTu6lXlWbz/mf/jlc/2+pV/XVn8Kz34RHPwl3niyR2FGYWyIVag70DKHzScFgY+ftju5xtRpSr0/FpuIExbXV3UqZuWxkbJJ9uCjnmlz+wOIKO1osiMSSruEZ4jqMt+4AZyus/9q0fozLF8bhDUmjcMZi8ftAUMGOf03rMRSZitCpdDS7mgGpgNfp7aTcmh3iClCZZ+IHlzXw1XPnTvi18VmuowKa3hOKa88e+PeHoXQxXPcv0I0q1GoNYMoDbfpxQTN472GGuB4HkKvEowNFjpZVGGBp0VL2DOwhEAlMO5kqNhXjj/gZCisH14BkFyw0Fr4rxrUsLVqKTWeb1FicvQN7qbBUTNvfeV35OvIN+fx5158TCgVNrqYZm/A0Q1ZT0xHXTApT6RTX8dRWkHpcU4YzBQbJ1acmrhXWCnL1ufFCkivo4iebfsLC/IVcN/e6cT97oji1/FS6vF3cuvFWvvnaNzGoDawqWTXh96m0VtLn74uTINneF1fjjmPY9XYElZ+nvrCOn1y1iJvPn8uGr6znU+sTVRi5GHFRQw05Ri2f+9c2Htzcyr/ebuWuV5u4cnk5a+tHiLrsrphMn6s/4k9SXBMw51z4v8fhmx2S+vHZTTD7XNjwvQTltSrPhEGr4kCXG51PSlAd6wYZ3eNaYEltES82FycUQJtcTdTljAqTUZjlOhFo1Spq8yTb8diROEfz3nzcwt0Jr/4C5l0M9ZMLZMsUzaNH4YyFrRRmnQ3b74cxLQ3ZhFalZXbu7HhhfyAwgD/iz5riOlXU2GsAaHG3xB/r90uziN/V6dev/xpUGrj+oZmk4BnEMUNcjwOMHeEQjUUZCg2RY8g5Kp+/vGg5kViE3f272TOwh3JL+bR9ttyD1OtNXfk/OHiQuXkTrzoeC2hUGtZVrOO1jteSegHHw57+7AczjYZOreNzyz7Htt5tPHvkWUDa8HZ4OmaCmaYZMnFVsgoPBgYREDIK9bBoLYrhSv6IH7NmfOJq1BoVFddwLMxQeCit4ioIAosLF7OjbweiKPLzd36OM+jku2u+O6JsZRGX1V/GjYtu5LHDj9Hh6eCOs+6YVPGqwiKRNLnPtdUtpelWWiuzd7DTBJvehjvkpirfxPtWVfGZ02dRlpOsKMjnRIHZxm+uW4ZaJfD1h3fxrUd3UWQ1cMuF8xOeLyeEyi0hE4GSVVgRegsYc6FwrhSSojPDY5+Jp3+qVQJziq10dR5BF5F6eMf2gMeJq6glPx1xHS6AukNuorEoLa6WxBRU+zChcE2urxdgYYk0Dq5lcOReJYoi7qB7psf16ZshFoFzfzjtH9XcLxXuFBVXgKUfkCzhTS9N63HMy5vHPsc+SW0dVjaPG+JqqwGkAo6MFldL1npwjwmGeqRRS8s+AJapjRyawXsLM8T1OEB8hMMwcXWH3IiIR+3muLRwKQDv9LzDrr5d06a2wkjIQSrLWjgWpsnVdFyPwRmL0ytOxxl0srU3OcE3FRwBB53ezmmzZMu4YtYVzMmdw882/4xnW57lvn33ATOJwtONdD2ug4FB7Hp7RuTPpFVOBfaGvRnNSzVrlXtcncOhM+kUV4DVpatpdjXzmRc+w38b/8tHGz7K/Pz5aV8zWcgzBP/fqf+Pe8+7l5NKT5rU+8jqotzn2uxuRqvSUmYpy9qxThdsOltGc1xH9zifNqeQ5798Go9/7lRe+Op6Xv7a6Umkr8RcwvKi5TzW+BjRWJQfb/oxl//3ci559JL4uZAKGRPX0bAUwVm3QscWaH0z/vDCMjuRrj3ohslsKquwXmXAnK7H1TxyH+n0dBKKhRKJq9YIpoJJK64AKyul82hz68h7eMNeImLkxCauex+DfY/DGd+CvNpp/7g9HW70GhVVeSkKdXMvkAom26fXLrwgfwHukJsub1fWRuFkCyatiRJzSdzKDNKIsnn5mSeyH3fYci/EwnDSJ471kczgOMMMcT0OkKvPRSWo4j2ug0Gp/+do2ZFyDDnU2ev4444/0untnPQsxkwgK66j+5NG44jrCOFYmDm5c6btGLKNdRXrsOls3LP7noxfM53BTKOhVqn5/trvo1VpuemVm/jttt+yvGg5q0tXT+vnnuiIK64KMzkHg4MZp+9atJapW4UVFFd5jRnPWfH+ee/nYw0f4/WO16m31/OpJZ/K6LgnC0EQuKT+kik5EeLEdbjPtdnVTLWt+l3R023X2xkKDY0bohRPlR5W3QVBYFGFnfpCC1q18m39itlX0OJu4Tsbv8N9++7DrrfT4m7hhdYX0n5Wqh7XcbH4WtDb4J17Rx6qsLMssgPd8LiNscRVOlcF8s3mtEpRiUlS4nt8PXGVqdY+hkTZK2DwyMSPexgrKqTzaGfXSN+gPB4nGyNQ3pXo3AZPfBlKl8Cazx2Vj9ze5qSh3I5Ok2K7qtFL43j2PQH+wWk7jtETGI434gqSq0K+FgYDg/T4epiX+y4lrkEPbP6LNOImf6bIPoNEHBPiKgjCNYIg7BEEISYIwspjcQzHE9QqNbn63LjiKlvcjuaieFKJpG7cuuZWLq67eNo+J24VTqG4Hhw8CMDsnHeP4mrWmvnYImlzv6VnS0avkYnrdKlXo9FQ0MCTVzzJH87+A/ddeB9/u+BvGQUDzWDySBfO5Aw6x1U6ZVi0KcbhhH2ZEVet1OM6lgjJKtt4s5rVKjVfWvEl7jnvHv54zh/HDYM6HpCrz8WsNY8orq7mZFJznMKusxMTY4rFitGQz4lMVHcZ51afi1lr5vGmxzm59GT+ev5fqbBU8Hzr82lfNynFFSSr8JLrYO9/wSsVZReV2zlFtZugWVJHQ2NThaMBVKI+bX8rjDh3erxpiGtxA3TtiFuVJ4oCk3SNHurvjj8mq+EnpOK6/0m490LQmuCqu7OW4rq7w8V1d73JlXdu5G9vtCR8LxyNsavDxdLKnPRvsvR6iAZh98NZOSYlzMmdg1pQs8+xj/ahdvIMeRO6/qYbtfZaml3NiKLIgUEpafzd0nKVhI2/Bm8frP/6sT6SGRyHOFaK627gSuDVY/T5xx0KjAVxxbV16Oj3ZH15xZd5/PLHuWbO9A4S16l15OpzUxLXQ85DaARNou3rXYD3z3s/hcZCfrv1txk9f0//HmpsNRmNRMkG1Co1p5af+q5Ian4vQKvWYtQYU1qFM934mrVmAtFAUoiNN+zFpBl/0yQ/Rx4zIsMRlMJxMj2OVSWr3hVhaSCpjxWWCtqG2ghFQ7QPtb9riKtNLxU8xgYCjUWm45BGw6Q1cVHtRRg1Rr67+rsIgsDZ1WfzdtfbiuepjHHDmdJhxUcgGoJ37gZgjjVMg9BCh05y1ChZhQVRR745fYGkwFSAgBBXXPMN+ckOpfJl4HdIybeTgFVrRUBFv99J35DUi+saHuNzwoUzvXkHPPABKJwHH38BCrJTWPaFInz+/m0c6vHQ7wnx8+cOEAiPZEXs7xoiGImNT1xLl0LRwmm1Cxs0Bmrttex37I/PcD2eUGurxR/x0+Pr4YBDIq6ySvyugrMN3vidpKJXTjycbwbvfRwT4iqK4j5RFA+M/8wTBwXGgrji2jbUhkljOqopmCatiUrb0SHK8kgcJRwcPEiNvQatWntUjiVbMGqMfHDBB9nauzUhkj4V9gzsmdZe4hkce1h1VkXFdTCQuVVYJiZjVVdvJPMeVyCpzzXe45rhcbzbII/EaRtqIypG3zXE1a6TCFE6Igkjf8+JKj43rbqJxy57LL7Wn119NpFYJG0quifswaKdpEOjeAHMvwRe/Rl07UTX9joqQWRbSCpMKoUzxWLpg5lASnnNN+bHiWtCorCMMmlkEJ0psgfCfoiElL+HVACxau0Iai+7OyTCKluFTyjF9Z174NlvSX/HDz8J1uKsvfWPn95Pc7+X31+/nP93xSKGAhE27BvZG2xvk6y/4xJXQZBCfDq2KM4Qzhbm5c1jU/cmdvTtOO6Iq3wNNLma2O/YT7Gp+Niv75EQHHhasnH3HczsNRu+J/171nen7bBm8O7GTI/rcYJ8Yz4DgWHF1d1Kla3q3ZsGNw6KTEVprcLvpv7W0Tij8gwAXmlPPxqn399Pj69nhri+x2HT2ZKIqyiKklV4gsR1rHU00x5X+TljidBg4Oj20R9tVFor6fB0cNh5GFCwkR6nmKjimonqPhpGjZFSS2n8/xcVLKLIWMTLbS+nfI0nNAXiCnDxb8CYBw/dAG/8joDKxGuDEvkJR8eMwwn7iUa15FvGt6QXm4rZN7CPZmezskOneCGotNChQFyDHvjDKfCLufD8rRAOJD8HyDPmIqh9HOiRrmPZKvxevW6S0Po2PHWz1Gt4zV9Blz1rrNMX4r63W/ng6irW1Oezpj6fUruBh7eMhGFta3NSYNFTkZtBj/Wia6XRKdv/mbVjHIv1FesxaoycWn4qH2748LR9zmQgr3HNrmb2O/Yfe7W1+TX446lw/3Xw4AfgjlXw8I3g6kj9mrZNsPs/sPbzkHP8p8DP4Nhg2oirIAgbBEHYrfB12QTf5xOCILwjCMI7fX1903W4xxz5xnz6/f2IokjrUCtV1qpjfUjTBnkG31i4Q266vd3vqkTh0ai111Jtqx53puvRCmaawbGFTWdLIoxD4SGiYjRjxUbuRR6dLCyKYsY9rnIv7VgiNBgcxKazoVW9u5wNmWJe3jyC0SD37paCgWpt7w7iGldcFUK9RsMX9qFX66ccOKUSVMzLnxdvTxmLcCxMIBqYWk+8OR+uvhsQoHMrPaVnMBSUjnus4joU8iFGdRSY0yuuAOfXnM9+x36GwkPKhQmNHkoapEChsdjwPXA0QcVK2PgbeO0Xip+RZ8hBrw9woFsirvFwJv0JEM40cBgeuF4Kubrqz5DlEVgvHeglGhO5ZoVEUNQqgcuXlfPqoX56h6RCwvZWJ0srczIr4lsKYfZ5sONBiEayeqwyzq89n1fe9wq/PP2Xx939O9+Qj1Vr5a2ut2h2NR+b/lZXO+x/SiKof7sYIn649h/wiVfg1C/Dvv/BHSdLKv7Y3vNYDJ75JlhK4JQvHf1jn8G7BtNGXEVRPFsUxQaFr8cm+D53iaK4UhTFlYWFhdN1uMccBYYCwrEwg8FBOoY6qLK9d4lrlbUKR8CRtJmW+zLerYorwGkVp7Gpe5NioI6MPf17EBCOSjDTDI4d7Hp7PL1XRqahSDLiimtoRHENxUJExWhGxNVukIiQrLDKmIhd+d2I82vOZ37efHYP7KbYVHxchaikg6zkjWcVzlRxzwQlphK6vd2K35PPuyn34tecCl/cDrf04LvoD4iiRFzHhjMNhbyIoo4C6/iK64cbPszjVzzOl1d8OXWgYNly6NwubYplNL8Gm/8Mqz8DH/g3NFwtkdfBlqSX5+hz0On8ceLqCrqwaC3v2YJPHN5++OdVIMak35Ex+2vF83t7KLLqWVQ+ol5fs6ICURT59YZDvH6on6Z+L6vrMlsrASmkydsLjRuyfrzHOwRBYG7eXF5ue5moGGVJ4ZKjewC7H4bfLoMH3i/NXz3ta/CZt2HBpVC2FM7+HnzmLahYISVTb/huInl97RfQ8Y70PP1MeOQMUmPGKnycQE7bfbvrbSJi5D2tuMozREfPHAPY1itVxo/6gptFnF5xOuFYmDc730z5nD0De6i112Zt4zmD4xP5xnwcfkfCY/ExNBkqrrICN7rIMxGbaDrFNdNk43cj1Co13zr5WwDvqqC3TK3CvohvwjbhVCg2F+MMOuMzVEdDJq5TsgqPhkbHrGIrWkEifsnjcPyIMS35GSiuANW2aj7a8NHU1t2yZRAagoFG6f+jYXjyq5BTDWd+W3rs3Nski+mztyS9PNcgWYUb+zxEojFcQdeJYRPe8D1JPbv+oawFMY1GMBLllQN9nDW/GJVqRE2tK7TwsVNr+dfbrXzpwW3UFZj54OrqzN94znnS/N5ptAsfz/j5+p/z1/P/ysOXPsy68nVH74Pf+gP856NQvlIK7/pao3R9jbWW59XCBx+FlR+TikX3nA+v/RJe+Sm8dLtk915y3dE77hm8K3GsxuFcIQhCO7AGeFIQhGePxXEcT1hduhqNSsNdO+8CeE8rrvJGUu4/k7Gtdxt19rp39cZgWfEyrForL7e/nPI5ewb2HHc2oxlkH3mGPAaDg0RjIymZsvKZqdopXwuyRREmligrE+Sxyu9gYHDcGa7vdiwtWsp3Vn/nuOtFSwe9Wo9BbchIcc2WiiynRfd4k9s3hsKS0pjN9HOtWkV9YQ6QYo5rTJdRj2tGqF4LCPDqTyV1Z9Nd0H8Azv/xyKbaVganfRX2PwGHX0x4uV1vJyQOEYpEaRnw4gw639X3p4zQvQu2/RNO/uS0pbq+1eTAG4py7oLkoKcvnzOHyjwjA94QP716MQbtBCzKai0sfh8ceCY+gulEQr4xnxXFK5iTO+foZKTEYvDcd+CZb0jhXTc8KtnvjTmpX6NSwUW/gPP+HwTd8ML3JdJaugQu+bUUtDWDGaTBMZnILorio8Cjx+Kzj1fkGHJYX7E+Pgz+vay4llnKMKgNHHaNENeYGGNH3w7OrT73GB7Z1KFVaTm1/FRebX+VmBhDJSTWhtqH2un399NQ0HCMjnAGRwt5hjxiYgxXyBW3BsvENVPFVX7eaAVOtqFnQlyNmv/f3n3Ht13d+x9/HUmWPCU7XkkcO84OWQQIYYYywgg7lE1LSxmXjltauqC9v3K5ZXRCuYXetkAvvbQFChSaAmXvFVbIIGSaTDuJHcd7yOP8/vhKjp3YxA6SvrL9fj4efsT66it9j5LjWB99zudz0vB7/D0CX3CWLA+HOXj+lPPdHsKABQPBfWdc+1nj3B8j0yOBa9N2SkOlPe6L2VLhPRwwMoctTb13Fbad+97Htd9yJ8BxP4KXbnKWv258w2k0NGVBz/OO+AZ8cD/86zr46htOAISzYqHDtoMnzOptDdS11g3tjsLWOh2E03LgmO/G7TKvr60k4PNwxIS9d05I9/u477K5fFLZyJzSASwTjpp9Mbx9Fyx/GA6/OgajlV61h2HRN2DZQ3DoFbDg5/2vgzYGjvi689W8Czo7nDkX4zpqGZq0VDiJnDnhTMB5s5mXlufyaOLHYzyMC43r2jgenOxrfbie2QWz3RtYjBxTfAzVLdUsr1q+132LKxYDcNiowxI9LEmw3DTnTVn35cLRAHIgNa4+4+s149qfjJsxhuxAdo9AyFpLdWv10H4DPogF/fsOXOORce2tzjWacY3ZUuGIGaOcn41dzT17AYQ7m6HTT056jDKuAPO+A1NOg01vOUHN2b/dO6vjCzhZ2KrVTlY2Ippd9foaKdtaQU39FkINVU6d7FC05hn45FU49vq41LV2XWZ7AxMLMvvMpk7Iz2R+L9nYfhk5w8nevfdHJyCS2GuthwcucILW4/8DTv3l/gedaTmQkaegVfpNgWsSmVc0j5xADsVZxUN2K5yo8dnjKavZHbhG61sPLjjYrSHFzLyieXiNt9fuwosrFpOflj+o6u5k/0T3YY5ucwXOkl2/x0+arx/bO+AEnqFAqPfAtZ81jtmp2T2aMzW2NdLe2T6ka1wHs1Ag1K99XGNV4xrtr9Bb4NpV4/pZugr3YuaYEVjroaJ2d9OxTttJm23B703D74vhWxOPBy64H75fBmfcAZkFvZ83+WQnG/vyT6HB2a4t+uHTpcHnuPzd06lpqiS05X2nY+rKAfWZTH4dbfDsf0DuJJhzWVwvtW6HE7jGzVHXOB9CrHg0ftcYrurK4b7ToOwVOPNOpwnTEH+/KslFgWsSSfGmcMORN/CN2d9weyhxNyE0gYrGiq434R/u+JARqSMozhr8e3eFAiFmF8zeaz9Xay2Lty1m7qi5Q/6DCdmdVa1u2Z1xjdaWDuTff8+MaWN7/2tce3v8QOtsJbGC/mDXfqF9aQg3xGz5bqovlZxATq9blEX3Ic5Kie1S4akjs8B62V6/e5unlnZnC5S4NK3zeMG/j+c1Bk65Fdqa4Z/fgqUPUlq+AoBZPM+7HEC910f2oVfDmEPhsauhYmn/rr/9I9j0thMcJqvXfgU718JJN3UtlY6HxtZ2ttY0MzE/joHrtIVQOBNevjW5/86TXc0mp0Pw0gedr7d/B/9zJFSthYsegIO/6PYIZRhypcZV+nZCyQluDyEhxmc7GcfofmNvlr/JnMI5QyagO674OH753i9ZXb26az+1tTVrqW6p5rCRWiY8HHRlXJt3Z1xrWmr6vUw4as9tdQZS4xp9/LqadV23o8+lwDU5hQIhPtr50aeeU99WH9NgcmRG71viRJcKZ+wr6BugrNQUPKRQ2bA74xr9EDPLzW7reZOc/SZf/TmsfpIiwD+2mDfz5/LwmovI4iZCwTFwwV/g7uPhgYvgyhcha2Tvz9fZAa/+Al75mbO1TGoIzrkHJidRLwdrnTG+fKvT1XXyyXG9XFml8+8c14yrxwPH/wgeuNCpdZ19cfyuNdSEm5zl8ksfgMpVe98/ajZ8/p64dJsW6Q8FruKK7p2FK5sq2dmykzMmnOHyqGLn7Ilnc/fyu/nFe7/g7hPvxhij+tZhJhgI4jXenhnX1l0Dri3NDmSzqX5T1+2BBq45gZzeM65aKpyUQv5QV6azN22dbTS3N8d0+W5hRiFbG7budbwh3ECaLy0u+5amePxUNe6ucW1qd74Ppbq8h+PxP4KDL4XONrzAuFevpdybj8fn/AzlpeVBVqGTcfrjyfDgJXDZU06dbHfhJnj0clj9FMy6EKae6gSID10CC34G4z4HI8Z/tmWW1WWwsZet14rn9i+wsBZe+C94/TY48GI46864L/tcV+nM7bgGrgCTT4H8qbD493DgRVrO2h81m+HBi2HbMig+3Kn7Ljnc+cAFAAPZJapHFVcpcBVXFGcVk+ZLY9H6RaT6UslLy+PooqPdHlbMhAIhvnbg17j1nVt5cfOLzCmcw/0r72di9kRGZ452e3iSAB7jYUTqiB6Ba01rDaMzBvbvn52azbKqZV23G9qcLFV/m/NEa2SjXa679pId4tvhDFbBQJDm9mbCHWH83r2bFDWGI5nJGHb6HZk+kve3v7/X8Ya2hpg3ZopK8/mpamvpqnfc0eDU9ZaOSIIPVLJ3l6yMz5nM0spl+DOcZkEz82c6d4yaBQt/D3/7Ijx/I5xyy+7HN1Q6zWvKl8CCX8BhVznHxx0Df/48PPFt53bhTDjwAujtZ9njg0knQXBUz+M71zuNpjYvhg//Cp3tvT/20Csgb3LfrzHc4NQprn8B5nwFTv2Vk6mMs3U7GvB6DGNz45xZNwbmXuns27v1fWebFunbxjfhoS9CR9jZvzfOmXeR/aXAVVzh8/i4fu71/PjNHwNw2YzL8HmG1nQ8b8p5PLzmYb73yveYkD2ByqZKfrXgV24PSxJoROqIHkuFq1uqB7xENzuQTU1rDdZajDHsatlFVkpWv7NgOak5dNpO6sP1ThDbUtM1Nkk+Ib+T3agL1/XaXb6r7jSGgWthRiH14Xqa2pp6fCBSH66PeWOmqGBqOlWmnedWbmdiQSYryp2GSFMLkquj/vjs8Ty94WlCuZ8Qttk9P3iadiYceqWz/UpGLmQUgO2A12+H+u1wwZ9h6mm7z0/Lga8842S0tn4A797rNETqi9cP085yuuSmZjs1te/90bmGJwUO+TLMvQp8qbsf0xGGN+6Axb/b94vLKYVjfwif+37CMpLrdjQwNjc9tg24+jLrQudDhXf+oMD106x6yvkAJqcULnwA8j/lAw8Rlw2tSEEGlYWTFrJy50oeXfsoCycudHs4MZfiSeGek+7hprdv4vlNz/OdQ76z+9N6GRa6Z1xrW2upD9dTmDGwbR6yA9m0d7bT1O7s3VnVXNXV7bS/jwcn2xsKhKhurSbFkxKzrrQSW9EtWGpba3sPXOOwRU3XljhN23p0PG8IN8S8MVNUekqAYJrhuZXb+OqxE/h4eyUA00f10fXXJeND47FYmn0r6KifSUenxeftFuSd9BPY8o6z5DYqPQ++/ETvwZI3BYoOcb4OvcLZX9Z27n1ec7UTcH38hFOnCWA8TrB6+NecIKOvJkpn3ek0WWpv7f3+6DjSE//h1bodDfFtzNRdIBNmXwLv3g3HfB/yJibmuoNJYxUs+nconA6XLoK0bLdHJPKpFLiKq3542A+5+sCrB/RGfDDJTcvltmNvY2vDVooyi9wejiRYblpuV33qJ7WfADAxe2BvnroHntHANT89f78eP5ax7GzeSU5qzpBphDbUBP1BgD73co1HxnVk+u69XHsErm2x6168p4A3QG6mhyVLa9hR18LaKmdlQkFmMC7X218TsicAYOkk3FjC6u31TB8d2n1CShpc8QLUd2tulZ4L/n58MGQMZPbxs5xVCKff7nw1VUO40emM3N9gMwkDkLaOTjbubOLk6X00s4qHedfCkj/Dsz+Cix9K3HUHA2vhX9+Hllr40j+Tcs6I7Enb4YirjDFDNmiNMsYwJmuMAoVhqHvGNdrZN/pGuL+iGbjoXq5VzVXkpfZ/OWVX4BpZIrylfgtjMscMaAySONF/7772co3urRrLgDJad1/RUNHjeH24Pm41rineFILpBmth0dJyNlQ7tdfJthKgJKsEr3Ga0XQ0lbK4rHrvk7wpTl1s9Ks/QetApI9wnteFDGkslVU20t5pmVSYwAZcmQXwue/Bmqdh3QuJu26y62iHf17j7HX7ue9D4TS3RyTSLwpcRUTiJDctl+b2Zpramlhfs540XxqjMkbt+4HdRAPP2hYnAzfgpcKRJkzRwHdL/RZKgiUDGoMkzj4zrm2xz7gWpBfgNd69OgvHM+Pq9/jxets5ckIuP39mNQ2RplP9bTqWKCneFEqCJaT70hkXmsiLq3a4PaRBa8VWZ07P6J6xToTDroaccfDMD52AbbhrbYAHL4IP/gRHXwvHfM/tEYn0mwJXEZE4iTZAqm6ppqy2jHGhcXjMwP7b7b7Ut6mtiab2pl5rH/v7+B3NOyjJUuCarIKB/i0VjmUm1OfxMTJj5N6Bazi+S4XbOtu4/YLZZAV8GI9Tj5lsgSvAyaUns3DSQuYfMIq3y3ZS19Lm9pAGpRXltaSleBmfqBrXKF/AqfmtXAXv/29ir90fLXWw7GH44H6nAVc81W+H+06Ddc87y9Dn36CtgmRQUeAqIhIn0cB1Z8tO1tesZ0JoYMuEYffS0V2tu6hqrgIYUI1rZkomPuOjprWGLQ1bACgOFu/jUeKWLH8WBrPPpcKxXsJblFlEeUN51+22zjZaOlriulQ43BGmMJjKby4+iImFfvwef1z2jP2svj7761w39zrmH1BIe6fl1TWVbg9pUFqxtZZpo4N4PS4ESlNPc7YjeulmaEiSrLm1TgfoX8+Av18Bi74B98yHT16Nz/Uq18C986FqjdM9eM5X4nMdkThScyYRkTiJLundULuB7U3bB1zfCj27zEYD14HUuBpjuvZy3Vy3GUAZ1yTmMR6y/Fl9ZlzrwnVkpGTg9Xhjet2izCJe3/p61+2uADlO2+EEvAFaO5ws65ET8jiyMpNnNiRftrW7g0tyyElP4YWPd3D6LO3HPRCdnZaPyus47xCX6uuNcfbU/cOx8MhX4IuPg9fFt8AdbbDom7D0rzD5FGfJbnouPPQFePAS+MrTTqffvrS3OvvTtjVBWwt0fEoHaYDyD2Hx7yE16HS8Ljokpi9HJFEUuIqIxMnYrLFk+bP4zZLfAANvzATOMs6slCxqWmuobHYyPQNtaJYdyGZXy66uDsfFWcq4JrNQINR3xrWtIS5Z0NGZo6lsrqS1o5WANxCXJlDdBbwBwh3hrtvN7c1J15hpT16P4bgpBby4egcdndadzOEgVVbVSFO4gxlFCa5v7a5gKpzxa3js3+DFn8CJN/Z9bkc7rH4KIj8HeHwwcX5sGmRZ62xBs/SBvffR/cIjTtb1wUvgqpd77/TbUgv3nwNb3xvARQ3MugBO+H8QUnM+GbwUuIqIxEmmP5NrD7mWG99y3iDtz1JhoCtj2pVxHUCNK8DEnIm8t+09QoEQI1JHxC0YkdgI+UN9ZlzjVXca3a6rvKGccaFxcdkvtruMlAwa2hq6bje1NSVlfeuejp1awN+XbGXZlhoOKslxeziDxkflkcZMbgauAAdeCBvfdJboTlkAJYfvfU57q5OVXfVEz+PBMXDuH6HksM82hjfuiASt18OxP+h5X2gMnHefU4f62L/BeX+ClNTd9zfsgL+eD9tWwBl3QME0p4bXG/j0WtVAEIIDawwokowUuIqIxNE5k85h0fpFrK5e3bXtyEBlB7Kpba1lZ/NOvMZLTurA3jDPHzufZzY8w3Mbn+uxT6ckp9y0XLY1buv1vvpwfUIC13hnXIP+IM3tzbR1tJHiTaGxrXFQBK5HT8zDGHhtbZUC1wFYsbWWgM/DpIIEN2bqzck3w/qX4PGvwUUPQN5kJ+hrqob373O2iNm+Ak6+Faae6jymZrNTg3rfafD5e2D62ft37VVPwfP/CdMXwud+0Ps5JYfDKT+Fp77rZF/nXgnGAx1heO1Xzjgv+DNMOWX/xiAyiClwFRGJI4/xcMdxd1DeUL7fdYmh1BDVzdXOVjipuQPuTHxM0TEEvAHqw/Wqbx0E8tLyWFG1otf76tvqyU2N/d7X0cA12lk43hnXru7J4Vry0vJoam9K+qXCACMy/MwsCvHqmkq+ecKkAT3WWst/v7COzbuauGXhTPy+4dMf890Nu5g2OojPmwSvOZAFZ98F9y+Eu+ZC9lgn+7r8EWiqgtEHOZnVGZ/f/ZicUrjqFSfb+chl8MkrMPYomHaWs49vf2xbDo9eAaNnw1m//fQM6dwrIbsEHrsa/vnN3cdDxXD5szBq1v68cpFBT4GriEic5aTmDDhL2t3UnKncV34fXuMdcH0rOFuMHDX6KF7c/KLqWweBvLQ8qluqae9sx+fp+Wu6PlxPabA05tfMT8/H5/F1Ba7xbs4U3a+2PlxPXloejW2NA14C75Z5k/L43Stl1LW0EUztfxfk3768ntufXwNAS1sHd1x40LCok91R18KHm2v4zomT3R7KbuOOgW8ugXUvwMp/OI2Lig6GS/8BI2f0/pi0bPjiY5H61IfgvT/C67fDkf++R/BqnKA2q3D3oS3vwV/OdZojXfgA+PvxIc3kk+HbH0HTzt3HMgucpcEiw5QCVxGRJLdg3ALuXXEvK3auYF7RvP16jhNLT3QCV22Fk/Ty0/KxWKpbqilIL+hxX7xqXD3Gw+iM0V1b4tS01gC7A8xYi76GaBOqwdCcKeqYSfnc9dJ63ly3k1NmjNzn+dFM6+3Pr2HhQUVMLsziZ0+voroxzK3nzGRsbkYCRu2eZ1duB+Ck6fv+u0qo7BKYc5nz1doAKeng2UdG2J/hZGM7O2DVk/DU95xa1L3Oy4KjrnHqSje+BSsegayRTuA7kFpTf3r/glyRYUKBq4hIkpucM5nxofGU1Zbtd1bqpLEnUdFQwXHFx8V4dBJreenOv3FVc1WPwNVaS31bfGpcwVkuHM24rtm1hvy0/K7tmGItGhDXtTqB62BpzgRw8NgcsgI+nl25rV+B643/XMl9b27gnIOL+NnnZ5Hi9ZCdnsItT37MmXe+wRvXHU9mYOi+HXt25XZKc9OZXJgE9a19CQxwbB4vTDvT6TRcu7nnfa0N8PIt8NJNzm1/Fsw8F47/cc8srIgM2ND9n1JEZIgwxrBg3ALu+vCu/Q5c/V4/V866MsYjk3iI/htHu0hHtXS00N7ZHre605JgCU+VPUWn7WRV9SqmjJgSl+vA7hrXaMZ1sDRnAkjxejj9wNE8vmQrN57ZRtanLBd+afUO7ntzA18+spQbzpiGidQ1XjS3hAn5mZz/+7d4esU2znVrf9M4q2tp4631VVx21Liu1z6k+NMhv5efk0segbpy6GyDzEJISUv82ESGoCSokhcRkX05ddypeI1XNarDQH5aPrB34BrvTr8H5h9IfVs9K3eupKymjKkjpsblOtAt4xquo62zjXBneNAsFQY4f84Ymts6eHJZRZ/nNLa28x+PrWBSQSbXnzp1r8Dt0NIcSkak8/iSrfu83scVdfz4HyvYUd/ymceeSH96YwNtHbZfmekhxRgIFTlNnRS0isSMAlcRkUGgJFjC42c9zukTTnd7KBJn0QZclU2VPY5HO/3GK3A9pPAQAP62+m+02/b4Zly7NWdqamsCnL1dB4vZxdlMKsjkofc2Y63d6/4ddS188d7FlNc289PPzyLg27ujuDGGsw8q4o31VWyv6zsgXbWtjovvfpv/e2sjZ9/5Bqu21cX0tcTLiq213PHCWk6fNYqDtXWQiMSAAlcRkUGiNFRKiqf/XUxlcAp4AwT9wb0yrvXh+G5RMzpzNKMyRvFk2ZOA0806XvxeP6neVOpa62hubwYYVBlXYwwXHFrMkk01HHbLC9z54tquAHZzdRNn3fUGH1fUc+dFB3PI2L6DtoUHFWEtPPrBll7vr2kK88V73yHg8/K7LxxMW6flew8vi8tr+qystTz4zibO+92bHPD/nuasu95gRIafn5zVR5deEZEBUo2riIhIkslPy0/4UmFwsq5PlD1Bmi8t7svSg/4gdeG6rozrYKlxjbrsqHFkp/t5Ylk5v3x2DRW1LZw2cxQ/fGw5ja3tPHz1Ecwo+vTmVuPyMpg3KY+7XlzHGbNGUzyi59/BT//ldB/+x9ePYkZRiC27mrnpyY8pq2xgfH5yNTtauqWW6/6+nCmFWVw4txi/z8PZs4vIyfC7PTQRGSKUcRUREUkyeWl5VDbvsVQ4HN+lwrB7ufDknMl4PXsvb42lYMAJXBvbGoHBtVQYwOsxnHvIGP73y4fyb8eM5y+LN3HxPYvZXtfK/142d59Ba9QtC2dijOE7Dy+ls3P3suN3PqnmwXc3c/nR47qe6/RZozEGFi0tj8tr+iweencTaSleHvnqEdxwxnSuX3AAB4yKz3ZKIjI8KXAVERFJMnnpeXsvFW6L71Jh2B24xrMxU1TQH3RqXNudjGuab3A2sTHGcN2CqfzzG0fzlysO47lrj/nU5cF7Kh6Rzo9Pn8Y7n1Tz13c2AdDa3sH1f19GUXYa35o/qevckaFU5paOYNHS8l5ra93S2NrOog/LOW3WqE/tsiwi8lkocBUREUkyealO4No9ONnZvBOA7NTsuF23NFjKZTMu45xJ58TtGlHRpcKDNePanTGGmWNCHDUxjzE5A1/yfN6cMRwxPpefP72KyvpWfv9KGesrG7lp4QzS/T2rus6cPZqyykY+Kk+eJk1PLq+gMdzBBYeq67mIxI8CVxERkSSTn55Pa0crDW0NXccqGivIS8sj4A3E7brGGK495Fqm5U6L2zWigoEgda11XRnXwdScKdaMMfzk7Bk0t3Vwwq9e5vbn13D6rFEcN6Vgr3NPme5sLfPy6h2JHmafHl+ylXF5GcwZQKZZRGSgFLiKiIgkma4tcbrVuVY0VDAqY5RbQ4q5LH/WoG7OFGsTCzK5eeFMjptawLdOmMzNC2f2el5uZoAZRUFeXVvV6/2JVtMUZvEn1SyYMXKvvWpFRGJJXYVFRESSTH5aPuAsDx4fGg84GdfJOZPdHFZMBf1BGtoa2Fi3kRRPCjkBZevOn1PM+XP2vdx23qR87n61jIbWdjID7r6Ve3HVDjo6LSdFMsEiIvGijKuIiEiSiWZWt9Q7+3taa6lorGB05mg3hxVTQb/TcXZxxWKmjphKildNffpr3qQ82jstb6/f6fZQeOajbYwMpjKrn12URUT2lwJXERGRJFOUWYTf46estgyA6pZqWjtaGZkxdLJawYATuK7etTohNbVDySFjc0hL8fLa2sp9nxxHzeEOXllTyYnTCvF4tExYROJLgauIiEiS8Xq8lIZKWV+zHnCWCQOMzhh6GVeA6bnTXRzJ4BPweTl8/AheW+dunes7G6ppaetk/rRCV8chIsODAlcREZEkNCE0oSvjGg1cR2UOreZMUdPzFLgO1NxxuZRVNrKrMezaGJZursEYOLgk27UxiMjwocBVREQkCY3PHk95QzlNbU2UN5QDDKmuwtGMa6o3tasBlfTfQZFg8cPNNa6NYenmGibmZ5KVqvpkEYk/Ba4iIiJJaHxoPBbLhroNbGvcRkZKRo/ltYNd9LUckHsAPo82ORioWWNCeD2GJZt2uXJ9ay1Lt9RwYHG2K9cXkeFHgauIiEgSmpA9AYD1NespbyhnVMaoIbVPZrQ5kxoz7Z90v4+pI7NY4lLGdcuuZqoawgpcRSRh9BGniIhIEirJKsFnfJTVllHRWDGklgkDpPnSuPHIGzls1GFuD2XQOqgkm38sKaez0ya8q+/SLTUAzB6TndDrisjwpYyriIhIEkrxplASLGFF1Qq2NmwdcoErwDmTzqEos8jtYQxaBxXnUN/azvrKhoRfe+nmGvw+D1NGZu37ZBGRGFDgKiIikqQmZE/g7Yq3qQvXMWXEFLeHI0km2qDpAxfqXD/cXMP00UH8Pr2VFJHE0FJhERGRJHX5zMuZkjOFo4qO0l6nspdxeRnkZwV4dU0VFxxakrDr1re0sWRTDZfPG5ewa4qIKHAVERFJUtNzpytglT4ZY5h/QCGLPtxKa3sHAZ83Idd9dU0V7Z2W+QcUJuR6IiKgpcIiIiIig9ZJ0wtpDHfw5vqdMX3eupY2Glrbe73vhVXbyU5P4SB1FBaRBFLgKiIiIjJIHTkhlwy/l2c/2h6z59xQ1ciJt73Cibe9wqptdT3u6+i0vLy6kmMn5+Pz6m2kiCSOK//jGGN+YYxZZYxZZox5zBiT7cY4RERERAazgM/LsVMKeP7j7YTbOz/z85XXNHPR3W8Tbu+k01rO+5+3WL6ltuv+JZt2Ud0Y5gQtExaRBHPro7LngBnW2lnAGuB6l8YhIiIiMqide8gYKutb+cGjy+jstJ/puW56ciU1TW385YrDeexrRxFMS+HqP7/PrsYwuxrDXPf35WSl+jhmcn6MRi8i0j+uBK7W2mettdHCibeBMW6MQ0RERGSwO25qAd89aTKPLdnKz55Ztd/Ps7hsJ08t38ZXj53AtNFBRmen8dtLDqayvpWz7nqD03/zOpuqm7j70jmE0lJi+ApERPYtGboKfwV4yO1BiIiIiAxWXz9uItvrWvn9K2UUZKVy0dxiADzGkJqy727D1lpueepjRoVSuXLe+K7jBxZnc8eFs/nz4o14jOGWc2Zy+PjcuL0OEZG+GGs/25KSPp/YmOeBkb3c9SNr7T8i5/wImAOcY/sYiDHmKuAqgJKSkkM2btwYl/GKiIiIDGYdnZav/eV9nunWqMlj4IYzpvOlI0s/9bGrt9Vz8q9f5b/Oms6lR3z6uSIi8WSMed9aO2fP43HLuFpr5+9jQF8GTgdO6CtojTzPH4A/AMyZMyc+UbaIiIjIIOf1GO648CAe/WALDS1ORdYrayq5+cmPObR0BNNGB/t87FPLKzAGFswYlajhiogMiCtLhY0xpwDfBz5nrW1yYwwiIiIiQ01qipdLDhvbdfu8OcWc/OtX+fZDH/LUNfPwekyvj/vXigrmlo4gPyuQqKGKiAyIW12F7wSygOeMMR8aY37n0jhEREREhqwRGX5uOGMaq7fX88LHve/1um5HA2u2N3DqTGVbRSR5uZJxtdZOdOO6IiIiIsPNKdNHMiqUyv1vb+Sk6Xu3H3l6RYVz3ozeWpOIiCQHtzKuIiIiIpIAPq+HLxw+ltfWVrFuR8Ne9z+1fBtzxuZQGEx1YXQiIv2jwFVERERkiLvg0GL8Xg/3vl7W4/iGqkZWVtSxQMuERSTJKXAVERERGeLyMgNcfFgJD727mdXb6ruO/2vFNkDLhEUk+SlwFRERERkGrjlhElmpKdz05EqiOxH+a0UFs4uzKcpOc3l0IiKfToGriIiIyDCQk+HnW/Mn8draKm57bg1/e3czy7bUcupMZVtFJPm50lVYRERERBLv0iNKWVVRz29eXAfAvEl5PfZ9FRFJVgpcRURERIYJr8dw6zkzGRlKpbG1nR8smEqKVwvwRCT5KXAVERERGUY8HsO3T5zs9jBERAZEH7GJiIiIiIhIUlPgKiIiIiIiIklNgauIiIiIiIgkNQWuIiIiIiIiktQUuIqIiIiIiEhSU+AqIiIiIiIiSU2Bq4iIiIiIiCQ1Ba4iIiIiIiKS1BS4ioiIiIiISFJT4CoiIiIiIiJJTYGriIiIiIiIJDUFriIiIiIiIpLUFLiKiIiIiIhIUlPgKiIiIiIiIklNgauIiIiIiIgkNQWuIiIiIiIiktQUuIqIiIiIiEhSU+AqIiIiIiIiSc1Ya90eQ78ZYyqBjW6PYx/ygCq3ByHDjuaduEVzT9yiuSdu0dwTtwyXuTfWWpu/58FBFbgOBsaY96y1c9wehwwvmnfiFs09cYvmnrhFc0/cMtznnpYKi4iIiIiISFJT4CoiIiIiIiJJTYFr7P3B7QHIsKR5J27R3BO3aO6JWzT3xC3Deu6pxlVERERERESSmjKuIiIiIiIiktQUuMaIMeYUY8xqY8w6Y8x1bo9HhhZjzB+NMTuMMSu6HRthjHnOGLM28mdO5Lgxxvx3ZC4uM8Yc7N7IZbAzxhQbY14yxqw0xnxkjLkmclzzT+LGGJNqjHnHGLM0Mu9ujBwfZ4xZHJlfDxlj/JHjgcjtdZH7S119ATLoGWO8xpglxpgnIrc19yTujDEbjDHLjTEfGmPeixzT79sIBa4xYIzxAncBC4BpwEXGmGnujkqGmPuAU/Y4dh3wgrV2EvBC5DY483BS5Osq4H8SNEYZmtqB71hrpwGHA1+P/P+m+Sfx1Aocb609EJgNnGKMORz4GXC7tXYisAu4PHL+5cCuyPHbI+eJfBbXAB93u625J4lynLV2drdtb/T7NkKBa2zMBdZZa8ustWHgQeAsl8ckQ4i19lWgeo/DZwF/inz/J+Dsbsf/zzreBrKNMaMSMlAZcqy1FdbaDyLf1+O8kStC80/iKDJ/GiI3UyJfFjgeeCRyfM95F52PjwAnGGNMYkYrQ40xZgxwGnBP5LZBc0/co9+3EQpcY6MI2Nzt9pbIMZF4KrTWVkS+3wYURr7XfJS4iCyBOwhYjOafxFlkqeaHwA7gOWA9UGOtbY+c0n1udc27yP21QG5CByxDya+B7wOdkdu5aO5JYljgWWPM+8aYqyLH9Ps2wuf2AETks7PWWmOMWoRL3BhjMoFHgW9Za+u6JxQ0/yQerLUdwGxjTDbwGDDV3RHJcGCMOR3YYa193xhzrMvDkeHnaGvtVmNMAfCcMWZV9zuH++9bZVxjYytQ3O32mMgxkXjaHl0SEvlzR+S45qPElDEmBSdo/Yu19u+Rw5p/khDW2hrgJeAInKVw0Q/du8+trnkXuT8E7EzsSGWIOAo40xizAaf063jgDjT3JAGstVsjf+7A+cBuLvp920WBa2y8C0yKdJzzAxcCi1wekwx9i4AvRb7/EvCPbscvjXSbOxyo7bbERGRAIrVa9wIfW2tv63aX5p/EjTEmP5JpxRiTBpyIU1/9EnBu5LQ95110Pp4LvGi1Ub3sB2vt9dbaMdbaUpz3cy9aay9Bc0/izBiTYYzJin4PnASsQL9vuxj9bMWGMeZUnJoIL/BHa+3N7o5IhhJjzAPAsUAesB24AXgc+BtQAmwEzrfWVkcCjTtxuhA3AZdZa99zYdgyBBhjjgZeA5azu97rhzh1rpp/EhfGmFk4TUi8OB+y/81a+1/GmPE4WbARwBLgC9baVmNMKnA/Tg12NXChtbbMndHLUBFZKvxda+3pmnsSb5E59ljkpg/4q7X2ZmNMLvp9CyhwFRERERERkSSnpcIiIiIiIiKS1BS4ioiIiIiISFJT4CoiIiIiIiJJTYGriIiIiIiIJDUFriIiIiIiIpLUfPs+RURERBIlsvXBC5GbI4EOoDJye661NuzKwERERFyk7XBERESSlDHmP4EGa+0v3R6LiIiIm7RUWERERERERJKaAlcRERERERFJagpcRUREREREJKkpcBUREREREZGkpsBVREREREREkpoCVxEREREREUlq2g5HREREREREkpoyriIiIiIiIpLUFLiKiIiIiIhIUlPgKiIiIiIiIklNgauIiIiIiIgkNQWuIiIiIiIiktQUuIqIiIiIiEhSU+AqIiIiIiIiSU2Bq4iIiIiIiCS1/w+l/PCEWGg5ZQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"###### Zipping the Outlier Plots Folder\nshutil.make_archive(\"Outlier_Plots_ECG1D_Incremental\", \"zip\", \"./Outlier Plots\")","metadata":{"execution":{"iopub.status.busy":"2021-07-20T08:15:40.362929Z","iopub.execute_input":"2021-07-20T08:15:40.363325Z","iopub.status.idle":"2021-07-20T08:15:40.393395Z","shell.execute_reply.started":"2021-07-20T08:15:40.363285Z","shell.execute_reply":"2021-07-20T08:15:40.392449Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Bandpass Matching","metadata":{}},{"cell_type":"code","source":"####### Filtering a specific Signal\n\n##### Plotting the Filtered Ouptut\nbp_filter = scipy.signal.butter(4,(1/180,110/180),'bp',output='sos')\nfiltered_op = scipy.signal.sosfilt(bp_filter,item_1)\nplt.plot(np.arange(512),item_1)\nplt.plot(np.arange(512),filtered_op)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-07-23T14:10:47.352398Z","iopub.execute_input":"2021-07-23T14:10:47.352713Z","iopub.status.idle":"2021-07-23T14:10:47.509937Z","shell.execute_reply.started":"2021-07-23T14:10:47.352685Z","shell.execute_reply":"2021-07-23T14:10:47.508935Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1152x576 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}